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IterSynth: Rethinking Deep Search Agents via Role-Decoupled Iterative Synthesis
Xingyu Wu, Yuchen Yan, Zhengxi Lu, Siqi Chen, Xin ZHANG, Aiting Liu, Chao Deng, Jie Liu, Jin Ma, Jian Shao, Jun Xiao, Yongliang Shen
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 95%
Last extracted: 9/25/2026, 5:29:10 AM
Summary
The paper introduces IterSynth, a role-decoupled deep search agent paradigm that alternates between a Planner and a Synthesizer using a shared LLM policy and a persistent summary state. It proposes Role-Decoupled Policy Optimization (RDPO) for training, which separates credit assignment for planning and synthesis roles. IterSynth-8B achieves state-of-the-art performance on long-horizon benchmarks like BrowseComp and Xbench-DS, outperforming previous agents under 8B parameters.
Entities (9)
Relation Signals (6)
IterSynth → trainedwith → RDPO
confidence 96% · To train IterSynth effectively, we further introduce Role-Decoupled Policy Optimization (RDPO) for reinforcement learning
IterSynth → usesrole → Planner
confidence 95% · IterSynth alternates between two roles instantiated by the same LLM. The Planner reasons over the compact state...
IterSynth → usesrole → Synthesizer
confidence 95% · The Synthesizer reads retrieved evidence and updates the persistent summary...
IterSynth-8B → achievesscoreon → BrowseComp
confidence 94% · IterSynth-8B achieves an average score of 50.7... on five long-horizon deep-search benchmarks such as BrowseComp
IterSynth-8B → outperforms → ReAct
confidence 93% · delivering substantial zero-shot gains over ReAct and similar prompting paradigms
IterSynth-8B → basedon → Qwen3-8B
confidence 92% · Full-parameter SFT is then performed on a single Qwen3-8B backbone
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Abstract
Abstract:Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents suffer from two limitations: role coupling, where one policy must handle planning, evidence use, and synthesis; and context accumulation, where growing search histories introduce noise and obscure useful information. To address these issues, we propose IterSynth, a role-decoupled and summary-based paradigm that alternates between a Planner for identifying information needs and a Synthesizer for integrating evidence into an evolving summary state. This design separates planning from synthesis while using the summary as the persistent state of search, reducing both capability coupling and context noise. To train IterSynth effectively, we further introduce Role-Decoupled Policy Optimization (RDPO) for reinforcement learning, which combines terminal outcome rewards with turn-level rubric evaluations and computes role-specific advantages for more precise credit assignment. Experiments on five long-horizon deep-search benchmarks such as BrowseComp and Xbench-DS show that IterSynth-8B achieves an average score of 50.7, surpassing the strongest prior $\leq$8B agent by +4.2\%. Moreover, IterSynth serves as a model-agnostic prompting paradigm, delivering substantial zero-shot gains over ReAct and similar prompting paradigms on frontier proprietary models.
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- Source: https://arxiv.org/abs/2609.29444v1
- Canonical: https://arxiv.org/abs/2609.29444v1
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IterSynth: Rethinking Deep Search Agents via Role-Decoupled Iterative Synthesis Xingyu Wu Affiliation: Zhejiang University Affiliation: Tencent Yuchen Yan Affiliation: Zhejiang University Zhengxi Lu Affiliation: Zhejiang University Siqi Chen Affiliation: Zhejiang University Affiliation: Tencent Xin Zhang Affiliation: Tencent Aiting Liu Affiliation: Tencent Chao Deng Affiliation: Tencent Jie Liu Affiliation: Tencent Jin Ma Affiliation: Tencent Jian Shao Affiliation: Zhejiang University Jun Xiao Affiliation: Zhejiang University Yongliang Shen Abstract Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents suffer from two limitations: role coupling, where one policy must handle planning, evidence use, and synthesis; and context accumulation, where growing search histories introduce noise and obscure useful information. To address these issues, we propose IterSynth, a role-decoupled and summary-based paradigm that alternates between a Planner for identifying information needs and a Synthesizer for integrating evidence into an evolving summary state. This design separates planning from synthesis while using the summary as the persistent state of search, reducing both capability coupling and context noise. To train IterSynth effectively, we further introduce Role-Decoupled Policy Optimization (RDPO) for reinforcement learning, which combines terminal outcome rewards with turn-level rubric evaluations and computes role-specific advantages for more precise credit assignment. Experiments on five long-horizon deep-search benchmarks such as BrowseComp and Xbench-DS show that IterSynth-8B achieves an average score of 50.7, surpassing the strongest prior ≤ 8B agent by +4.2%. Moreover, IterSynth serves as a model-agnostic prompting paradigm, delivering substantial zero-shot gains over ReAct and similar prompting paradigms on frontier proprietary models. †date: September 24, 2026†Code: https://github.com/Tencent/IterSynth†correspondence: wuxingyu,syl@zju.edu.cn, daniellwang@tencent.com 1 Introduction Deep search extends LLMs from passive retrieval to active knowledge construction. Given a complex query, a deep-search agent must decompose the problem, issue searches, read evidence, refine information needs, and synthesize a grounded answer (OpenAI, 2025a; Google, 2025; xAI, 2025; Perplexity, 2025; Anthropic, 2025; Tongyi DeepResearch Team et al., 2025). Recent open-weight agents, inspired by frontier systems, typically follow a ReAct-style recipe (Yao et al., 2023): they are first trained with SFT on search-and-reasoning trajectories and then optimized with outcome-driven RL or preference objectives (Tao et al., 2025; MiroMind Team et al., 2026; Chen et al., 2026b; Du et al., 2026; Zhang et al., 2025). However, most of them still rely on a single policy to drive a linear sequence of reasoning, search, and synthesis actions. This single-policy linear recipe becomes fragile as the search horizon grows. First, deep search involves heterogeneous abilities such as planning, query formulation, evidence filtering, gap identification, conflict resolution, and final synthesis. Forcing one undifferentiated policy to handle all of them can cause premature termination, redundant searches, or shallow evidence use. Second, long-horizon search continuously accumulates retrieved passages, intermediate thoughts, and partial conclusions, making useful evidence harder to locate while letting early mistakes propagate to later decisions; we empirically observe in Appendix H that even with a 64K context, ReAct trajectories on BrowseComp fail to terminate before context exhaustion in over 59% of cases. Prior work mainly addresses these issues through multi-agent or summary-based systems. Multi-agent methods assign different subtasks to different models (Figure 1a) (Luo et al., 2025; Li et al., 2025b), reducing cognitive burden but increasing inference cost, deployment complexity, and coordination overhead. Summary-based wrapfigurer Structural comparison of long-horizon reasoning paradigms. IterSynth uniquely combines a single parameter set with capability-decoupled Planner–Synthesizer roles, achieving the specialization benefit of multi-agent systems and the bounded-context benefit of summarization within one shared policy. agents periodically compress search history into compact memory (Figure 1b) (Chen et al., 2026a; Ye et al., 2025; Wu et al., 2026; Yu et al., 2025), but summarization is usually treated as an external auxiliary module rather than a learned part of the search policy. Thus, these methods alleviate context accumulation but still couple planning and evidence use, and external summaries may discard useful information or preserve misleading noise. These limitations call for a unified paradigm that separates planning from synthesis while making summary updates an explicit and optimizable component of search. We propose IterSynth (Figure 1c), a role-decoupled and summary-based paradigm for deep search. IterSynth alternates between two roles instantiated by the same LLM. The Planner reasons over the compact state, identifies unresolved information needs, and generates the next query. The Synthesizer reads retrieved evidence and updates the persistent summary by integrating useful findings, resolving inconsistencies, and filtering noise. In this way, the summary becomes the evolving state of search rather than a passive compression artifact. IterSynth thus gains the specialization benefit of multi-agent systems without extra models, and the context-control benefit of summary-based agents while making summary updates part of the agent’s behavior. We further develop an SFT-to-RL training recipe for IterSynth. The cold-start SFT stage teaches the Planner–Synthesizer protocol and produces valid iterative search trajectories, but imitation mainly learns the interaction format and does not explicitly optimize when to search, what evidence to preserve, or how the two roles should coordinate. We therefore introduce Role-Decoupled Policy Optimization (RDPO), which combines terminal outcome rewards with turn-level rubric evaluations and computes group-relative advantages separately for each role. This provides more precise role-specific credit assignment while retaining the simplicity and scalability of standard policy-gradient training. Experiments show that IterSynth-8B achieves an average score of 50.7%50.7\% across five long-horizon deep-search benchmarks, including GAIA-text-only, xBench-2505, xBench-2510, BrowseComp, and BrowseComp-ZH, surpassing the strongest prior ≤ 8B agent by +4.2%+4.2\% and remaining competitive with several 3030B-scale agents at less than one third of the parameter budget. Further analysis shows that role-decoupled training is crucial: outcome-only GRPO improves the SFT baseline from 44.1%44.1\% to 48.9%48.9\%, while RDPO further raises it to 50.7%50.7\%, especially on exploration-intensive benchmarks. IterSynth also generalizes as a prompting strategy for frontier models such as Claude-4.5-Opus and DeepSeek-V3.1, consistently outperforming ReAct and IterResearch, with up to +10.0%+10.0\% over ReAct on BrowseComp-ZH. In summary, our contributions are: • We propose IterSynth, a role-decoupled and summary-based deep-search paradigm that alternates between a Planner and a Synthesizer within a single shared LLM policy, addressing capability coupling and context accumulation without increasing parameter count. • We introduce Role-Decoupled Policy Optimization (RDPO), which combines terminal outcome rewards with turn-level rubric rewards and computes group-relative advantages independently for each role, enabling more precise credit assignment in multi-role search trajectories. • We provide comprehensive experiments on five long-horizon benchmarks, showing that IterSynth-8B establishes a strong frontier among small trained agents, that role-decoupled RL is essential for reliable long-horizon search, and that IterSynth also serves as an effective prompting paradigm for frontier proprietary models. 2 Related Work 2.1 Search Agents Training Building on the rapid progress of LLM-based agents (Yao et al., 2023; Schick et al., 2023), deep research has emerged as a frontier application in which an agent autonomously plans, navigates multi-turn web interactions, and synthesizes evidence into a final answer. Proprietary systems such as OpenAI Deep Research (OpenAI, 2025a), Gemini Deep Research (Google, 2025), Grok DeepSearch (xAI, 2025), Perplexity (Perplexity, 2025), and Claude (Anthropic, 2025) achieve strong long-horizon performance through large-scale agentic training and tightly integrated tool use. Open-source efforts such as Tongyi DeepResearch (Tongyi DeepResearch Team et al., 2025) and MiroThinker (MiroMind Team et al., 2026) seek to narrow this gap by combining supervised fine-tuning on synthesized multi-turn trajectories with outcome-driven reinforcement learning to optimize tool use, retrieval planning, and answer accuracy (Tao et al., 2025; Lu et al., 2025; Liu et al., 2025; Chu et al., 2026). Despite this progress, training algorithms alone leave critical bottlenecks unresolved, as they remain bound to the agent workflow in which the trained policy is deployed. 2.2 Agent Workflow of Deep Search Most open-source deep search agents adopt the ReAct workflow (Yao et al., 2023), where a single policy interleaves reasoning, search, and synthesis within one ever-expanding context, suffocating the reasoning space and embedding early errors into the trajectory. Recent work explores alternative workflows centered on context management: IterResearch (Chen et al., 2026a) rebuilds a focused workspace from an evolving report at each step; AgentFold (Ye et al., 2025) performs proactive context folding to maintain a compact working memory; and ReSum (Wu et al., 2026) periodically condenses the exploration history via a summarize-and-reset workflow. A complementary line decomposes the workflow across specialized components, exemplified by InfoFlow (Luo et al., 2025), which alternates between a researcher and a refiner to separate evidence gathering from consolidation. However, these workflows address context suffocation and capability entanglement in isolation rather than jointly, leaving the gap that IterSynth fills by combining role-decoupled planning and synthesis with iterative workspace reconstruction within a single shared policy. Table D (Appendix D) contrasts IterSynth against representative baselines along four architectural dimensions, showing that no prior single-policy method jointly achieves role decoupling and workspace reconstruction under a bounded context. Figure 1: Overview of the IterSynth pipeline. At each iteration, a shared policy alternates between a Planner sub-step that issues a search query or a final answer conditioned on the global summary, and a Synthesizer sub-step that integrates the retrieved evidence into an updated summary. The active context is reconstructed from (q,Mt+1)(q,M_t+1) at the beginning of every iteration. 3 Methodology In this section, we present our methodology for building long-horizon deep research agents. We first introduce IterSynth (Section 3.1), a structured workflow that decomposes deep research into an iterative interaction between two specialized roles: the Planner and the Synthesizer. We then describe a training recipe for instantiating a native IterSynth-style search agent (Section 3.2). It begins with cold-start supervised fine-tuning to teach the role format and iterative behavior, followed by Role-Decoupled Policy Optimization (RDPO) to further optimize role-specific decision making under long-horizon feedback. 3.1 IterSynth: A Dual-Role Iterative Deep Search Workflow As illustrated in Figure 1, IterSynth is designed as a structured workflow for long-horizon deep search. Instead of performing search, reasoning, evidence aggregation, and answer generation within a single monolithic context, IterSynth decomposes the process into an iterative loop between two specialized roles: a Planner and a Synthesizer. At each iteration, the Planner determines the next search direction based on the current research state, while the Synthesizer integrates newly retrieved evidence into a persistent global summary. This dual-role design separates exploration from consolidation, enabling the agent to refine its search trajectory while maintaining a compact and reliable memory. 3.1.1 Role Definition IterSynth separates the Planner and the Synthesizer by their responsibilities, information access, and admissible actions. The two roles are executed by the same policy with shared parameters, distinguished only by role-specific prompts and action constraints. Planner. The Planner controls the search trajectory. At each iteration, it observes the original question and the current global summary, and decides whether to issue a new search query or terminate with a final answer. It focuses on strategic exploration: identifying missing information, formulating searchable sub-questions, and judging whether the collected evidence is sufficient. Synthesizer. The Synthesizer maintains the memory. Given the retrieved evidence, it updates the global summary by filtering irrelevant passages, extracting useful findings, and resolving local inconsistencies. It can only update the memory, and cannot issue queries or produce the final answer. This separation lets a single shared policy behave as two specialized roles: the Planner decides where to search next, while the Synthesizer determines how newly retrieved evidence is incorporated into the agent’s memory. 3.1.2 Dual-Role MDP Formulation We formalize IterSynth as an augmented Markov Decision Process specified by the tuple ⟨,,ℰ,,ℛ⟩ ,D,E,T,R . This formulation describes the interaction structure of IterSynth. Each iteration t consists of two consecutive role-conditioned sub-steps: a Planner step (P)(P) followed by a Synthesizer step (S)(S). Both roles are executed by the same policy model πθ _θ with shared parameters, but are conditioned on different role prompts and constrained by different action spaces. State Space S. The agent maintains a global summary MtM_t as its persistent memory. At iteration t, the state is defined as st=(q,Mt), s_t=(q,M_t), (1) where q is the original user question and MtM_t contains the consolidated evidence collected so far. The Planner only observes this compact state, rather than the full history of previous queries, retrieved documents, and reasoning traces. Decision Space D. At each state sts_t, the shared policy πθ _θ produces two role-conditioned decisions in sequence, controlled by the Planner prompt ρPρ^P and the Synthesizer prompt ρSρ^S. In the Planner sub-step, the policy conditions on the current state and outputs a reasoning trace together with an executable action: dtP=[ThinktP,at]∼πθ(⋅∣st,ρP). d_t^P= [\,Think_t^P,\,a_t\, ] _θ(· s_t,ρ^P). (2) The action is selected from the planning action space, at∈search(qt),answer(a^)a_t∈\ search(q_t), answer( a)\. A search action issues a new query qtq_t to the environment, while an answer action returns the final answer a a and terminates the trajectory. In the Synthesizer sub-step, if the Planner chooses to search, the environment returns retrieval results tTR_t. The policy then conditions on the current state, the Planner decision, and the retrieved evidence to produce a reasoning trace and an updated global summary: dtS=[ThinktS,Mt+1]∼πθ(⋅∣st,dtP,t,ρS). d_t^S= [\,Think_t^S,\,M_t+1\, ] _θ(· s_t,d_t^P,TR_t,ρ^S). (3) The Synthesizer is restricted to memory update and cannot issue new search queries or produce the final answer. Figure 2: Overview of Role-Decoupled Policy Optimization. Per-turn composite rewards are first computed from a globally broadcast outcome signal and role-specific rubric scores, and are then partitioned into two role-specific groups, within which advantages are normalized independently before being fed into the standard policy update. Environment ℰE and Transition T. If at=search(qt)a_t= search(q_t), the environment returns retrieved documents t∼ℰ(⋅∣at)TR_t (· a_t). The transition then updates the research state by replacing the previous memory with the synthesized summary: st+1=(st,dtP,t,dtS)=(q,Mt+1). s_t+1=T(s_t,d_t^P,TR_t,d_t^S)=(q,M_t+1). (4) If at=answer(a^)a_t= answer( a), the trajectory terminates and the Synthesizer sub-step is skipped. A complete trajectory τ therefore alternates between Planner and Synthesizer sub-steps until the Planner emits a final answer. Since both roles share the same policy parameters, IterSynth introduces role specialization through prompts, information access, and action constraints, without requiring separate Planner and Synthesizer models. The complete iterative procedure is summarized in Algorithm 1 (Appendix B.1). 3.2 Training Recipe To build a native IterSynth-style deep search agent, we propose a two-stage training recipe: cold-start supervised fine-tuning for structural initialization, followed by reinforcement learning for optimizing long-horizon search behavior. 3.2.1 Cold Start via Supervised Fine-Tuning The supervised fine-tuning (SFT) phase initializes the shared parameters under a single-model, dual-task paradigm. The training corpus aggregates public deep-search datasets with curated synthetic real-world instances. For each question, we synthesize a multi-turn Planner–Synthesizer trajectory by prompting a frontier foundation model (Qwen3.5-397B-A17B) to roll out the dual-role loop in a live search environment. Raw trajectories then undergo a filtering pipeline that repairs invalid tool calls, removes hallucinated reasoning steps, and retains only trajectories ending in verifiably correct answers, yielding roughly 1010K high-quality trajectories. Each trajectory is unrolled into per-turn samples, where every Planner and Synthesizer response forms an independent supervised target conditioned on its role-specific prompt and reconstructed workspace. Full-parameter SFT is then performed on a single Qwen3-8B backbone over this expanded sample set; further details are deferred to Appendix B.3. 3.2.2 Role-Decoupled Policy Optimization While SFT instills the structural format, reinforcement learning is further required to optimize strategic exploration. A distinctive feature of IterSynth is that each trajectory naturally decomposes into multiple independent interaction rounds, which RDPO exploits to deliver fine-grained, role-aware gradient signals (Figure 2). Multi-Round Trajectories and Composite Rewards. For a given query q, the policy executes G independent rollouts. Each trajectory i unfolds over TiT_i iterations, producing state-decision tuples (si,t,di,tP,di,tS)(s_i,t,d_i,t^P,d_i,t^S) and yielding a corpus q=(si,t,di,tP,di,tS)∣i∈[1,G],t∈[1,Ti]C_q=\(s_i,t,d_i,t^P,d_i,t^S) i∈[1,G],\,t∈[1,T_i]\ of ∑i=1GTi _i=1^GT_i interaction rounds rather than only G trajectory-level samples. To overcome terminal-reward sparsity, we adopt a composite reward combining turn-level rubric scoring with a broadcast trajectory-level correctness signal. The terminal accuracy reward racc,i∈0,1r_acc,i∈\0,1\ is determined by the final answer and broadcast uniformly across all turns of trajectory i, while an LLM judge scores each intermediate behavior along five predefined dimensions, producing turn-level rubric rewards rrubric,i,tPr_rubric,i,t^P and rrubric,i,tSr_rubric,i,t^S for the Planner and Synthesizer; the rubric construction and reward-model setup are detailed in Appendix C. The composite reward for role ρ∈P,Sρ∈\P,S\ at turn t of trajectory i is ri,tρ=racc,i+α⋅rrubric,i,tρ, r_i,t^ρ=r_acc,i+α· r_rubric,i,t^ρ, (5) where α is a scaling coefficient. Role-Decoupled Group Advantage. Normalizing advantages across mixed roles entangles credit assignment. RDPO instead partitions the composite rewards into two role-specific pools, each aggregating all turns and rollouts associated with query q, and normalizes advantages strictly within each pool. For query q and role ρ, the reward pool is ℛqρ=ri,tρ|i∈[1,G],t∈[1,Ti], ^ρ_q=\\,r_i,t^ρ\; |\;i∈[1,G],\,t∈[1,T_i]\,\, (6) and the advantage of role ρ at turn t of rollout i is Ai,tρ=ri,tρ−μqρσqρ, A^ρ_i,t= r_i,t^ρ-μ^ρ_qσ^ρ_q, (7) where μqρμ^ρ_q and σqρσ^ρ_q are the mean and standard deviation of ℛqρR^ρ_q. The decoupled advantages then plug directly into the standard Group Relative Policy Optimization objective without further algorithmic modifications. Table 1: Performance comparison of Foundation Models with Tools, Trained Agents (≥ 30B), and Trained Agents (≤ 8B). Best results are in bold, and second best are underlined. Model Browse Browse GAIA xBench- xBench- Average Comp Comp-ZH text-only DS-2505 DS-2510 Foundation Models with Tools Claude-4.5-Opus 67.8 62.4 – – – 65.1 OpenAI GPT-5 High 54.9 65.0 76.4 77.8 75.0 69.8 OpenAI-o3 49.7 58.1 – 67.0 – 58.3 Gemini-3.0-Pro 59.2 66.8 – – 53.0 59.7 Minimax-M2.1 62.0 47.8 64.3 68.7 43.0 57.2 DeepSeek-V3.2 67.6 65.0 75.1 78.0 55.7 68.3 Kimi-K2.5 74.9 62.3 – – 46.0 61.1 GLM-4.7 67.5 66.6 61.9 72.0 52.3 64.1 Trained Agents (≥ 30B) ReSum-30B 18.3 33.3 48.5 – – 33.3 AgentFold-30B-A3B 36.2 47.3 67.0 – – 50.2 OpenSeeker-30B-SFT 29.5 48.4 – 74.0 – 50.6 IterResearch-30B-A3B 37.3 45.2 72.8 71.0 – 56.6 WebSailor-V2-30B 35.3 44.1 74.1 73.7 – 56.8 Tongyi-DR-30B 43.4 46.7 70.9 75.0 55.0 58.2 MiroThinker-v1.0-30B 41.2 47.8 73.5 70.6 – 58.3 MiroThinker-v1.5-30B 56.1 66.8 72.0 73.1 – 67.0 MiroThinker-v1.7-mini 67.9 72.3 80.3 – 57.2 69.4 Trained Agents (≤ 8B) OffSeeker-8B-DPO 12.8 26.6 51.5 49.0 – 35.0 WebExplorer-8B-RL 15.7 32.0 50.0 53.7 23.0 34.9 AgentCPM-Explore-4B 24.1 29.1 63.9 70.0 34.0 44.2 MiroThinker-v1.0-8B 31.1 40.2 66.4 60.6 34.0 46.5 IterSynth-8B (Ours) 30.9 55.4 55.3 66.0 46.0 50.7 4 Experiments 4.1 Experimental Setup Datasets. We evaluate on five long-horizon deep search benchmarks: BrowseComp (Wei et al., 2025), BrowseComp-ZH (Zhou et al., 2025), GAIA (Mialon et al., 2023), and Xbench-DeepSearch (the 2505 and 2510 splits) (Xbench-Team, 2025), which together cover multi-step tool use, web navigation, complex reasoning, and cross-lingual information synthesis. Baselines. We compare against three groups: (1) foundation models with tools, including GLM-4.7 (GLM Team et al., 2025), Minimax-M2.1, DeepSeek-V3.2 (DeepSeek-AI et al., 2025), Kimi-K2.5 (Kimi Team et al., 2026), Claude-Opus, OpenAI-o3, GPT-5 High, and Gemini-3-Pro; (2) trained agents at or above 3030B, including Tongyi DeepResearch (Tongyi DeepResearch Team et al., 2025), WebSailor-v2 (Li et al., 2025a), the MiroThinker series (MiroMind Team et al., 2026), AgentFold (Ye et al., 2025), OpenSeeker-30B-SFT (Du et al., 2026), and ReSum (Wu et al., 2026); and (3) trained agents at or below 88B, including OffSeeker-8B-DPO (Zhou et al., 2026), WebExplorer-8B-RL (Liu et al., 2025), AgentCPM-Explore-4B (Chen et al., 2026b), and MiroThinker-v1.0-8B (MiroMind Team et al., 2026). Implementation. We use Qwen3-8B (Yang et al., 2025) as the backbone and follow the two-stage recipe in Section 3: SFT on roughly 1010K question–answering samples curated from open-source datasets and synthesized real-world instances, followed by RDPO on a moderate-difficulty subset selected by the SFT policy. The agent interacts with the environment through a search engine and a web browser, with cached retrieval used during RL rollouts and live tools used at evaluation. Full details on data construction, tool environment, training hyperparameters, and reward-model configuration are provided in Appendix B. 4.2 Main Results Table 1 reports the overall performance of IterSynth-8B and representative baselines on five long-horizon deep search benchmarks. IterSynth-8B reaches a strong frontier among small trained agents. Within the ≤ 8B category, IterSynth-8B attains the best average score of 50.7%50.7\%, improving over the strongest prior small agent MiroThinker-v1.0-8B by +4.2%+4.2\% . The most pronounced improvement appears on BrowseComp-ZH, where IterSynth-8B reaches 55.4%55.4\% and surpasses the strongest small-agent baseline by +15.2%+15.2\%, indicating that the proposed framework is particularly effective on the most exploration-intensive long-horizon tasks. The model also yields a +5.4%+5.4\% gain on xBench-DS-2510 and remains competitive on BrowseComp and xBench-DS-2505. IterSynth scales effectively beyond its size category. Despite using only 88B parameters, IterSynth-8B remains competitive with several 3030B-scale agents: it surpasses ReSum-30B, AgentFold-30B-A3B, and OpenSeeker-30B-SFT in average score, and approaches IterResearch-30B-A3B and WebSailor-V2-30B at less than one third of the parameter budget. This suggests that the IterSynth pipeline offers a structural foundation that allows a moderately sized agent to match the long-horizon performance typically associated with substantially larger models. The training recipe is essential for unlocking long-horizon capability. The advantage of IterSynth-8B is not driven by architecture alone. Compared with prior ≤ 8B agents trained under DPO or outcome-only RL, such as OffSeeker-8B-DPO and WebExplorer-8B-RL, the proposed agent improves the average score by a clear margin, with noticeably larger gains on exploration-heavy tasks where credit assignment is most difficult. This indicates that IterSynth provides the structural foundation, while RDPO, through role-decoupled rubric rewards and per-role group advantages, converts this prior into stable long-horizon behavior at moderate model scale. Appendix J further shows that a lightweight not-attempted resampling strategy translates extra inference compute into consistent accuracy gains. 5 Analysis 5.1 IterSynth Is an Effective Workflow for Search Agents wraptable [16]r Training-free prompting comparison across four long-horizon benchmarks. BCzh: BrowseComp-ZH; Xb05/Xb10: Xbench-DeepSearch 2505/2510. Workflow GAIA Xb10 BCzh Xb05 Avg Backbone: Claude-4.5-Opus ReAct 58.3 55.0 60.2 69.0 60.6 IterResearch 66.0 53.0 61.9 72.0 63.2 IterSynth 61.2 57.0 70.2 76.0 66.1 Backbone: DeepSeek-V3.1 ReAct 51.5 31.0 39.1 52.0 43.4 IterResearch 51.8 33.0 40.8 55.0 45.2 IterSynth 56.8 35.0 43.6 56.0 47.9 Table 5.1 evaluates whether IterSynth can improve general-purpose LLMs as search agents under a purely prompting-based setting, without any parameter updates. Across both Claude-4.5-Opus and DeepSeek-V3.1, IterSynth achieves the best average performance among the three workflows. Compared with ReAct, IterSynth improves the average score by +5.5%+5.5\% on Claude-4.5-Opus and +4.5%+4.5\% on DeepSeek-V3.1, indicating that a mono-contextual search process is insufficient for long-horizon information seeking. Compared with IterResearch, which also performs iterative context reconstruction, IterSynth remains stronger on most benchmarks, especially on BrowseComp-ZH and Xbench, where sustained evidence accumulation and cross-step synthesis are critical. These results show that IterSynth is not only effective after training, but also provides a better workflow for prompting frontier LLMs to act as search agents. Its Planner–Synthesizer decomposition separates search control from evidence integration, reducing the burden on a single model context to simultaneously decide where to search, what to retain, and how to consolidate partial findings. This division also makes the search process more robust to long-horizon exploration, as intermediate evidence can be progressively organized rather than simply appended or periodically compressed. The consistent gains across two different backbones further suggest that IterSynth captures a general structural prior for deep search, rather than relying on model-specific behavior. Overall, the results validate IterSynth as an effective workflow for long-horizon search agents. Appendix K provides a case on BrowseComp . 5.2 RDPO Enables Effective Optimization for IterSynth-style Search Agents Table 2: Ablation on training methodology. w/o RD uses the same composite reward as RDPO but normalizes advantages across mixed-role samples. BC BCzh GAIA Xb05 Xb10 Avg IterSynth-RDPO 30.9 55.4 55.3 66.0 46.0 50.7 – w/o RD 27.9 52.1 50.1 61.0 45.0 47.2 IterSynth-GRPO 28.2 53.6 50.5 63.0 49.0 48.9 IterSynth-SFT 23.7 50.1 47.6 61.0 38.0 44.1 Table 3: Role-swap ablation. Each role is independently replaced with the untrained Qwen3-8B model while the other retains the IterSynth-8B checkpoint. Configuration Xb05 BCzh GAIA Avg IterSynth-8B (full) 66.0 55.4 55.3 58.9 Synth. → Qwen3-8B 55.0 35.4 34.0 41.5 Δ vs. full −11.0-11.0 −20.0-20.0 −21.3-21.3 −17.4-17.4 Planner → Qwen3-8B 27.0 7.9 18.5 17.8 Δ vs. full −39.0-39.0 −47.5-47.5 −36.8-36.8 −41.1-41.1 Table 3 shows that RDPO provides an effective RL optimization recipe for the IterSynth workflow. Starting from the structurally initialized IterSynth-SFT model, outcome-only GRPO already improves the average score from 44.144.1 to 48.948.9, indicating that reinforcement learning can optimize search behavior beyond imitation. RDPO further raises the average score to 50.750.7, with consistent gains on most benchmarks, especially BrowseComp and BrowseComp-ZH. The comparison with w/o RD highlights that these gains do not come merely from adding turn-level rubric rewards. Although w/o RD uses the same composite reward as RDPO, it normalizes advantages across mixed-role samples and drops to 47.247.2, even below the outcome-only GRPO baseline. This suggests that dense Synthesizer-side rewards and sparser Planner-side rewards should not share a single baseline, as doing so entangles their credit signals. By computing advantages within role-specific groups, RDPO preserves clean credit assignment for each role and enables stable end-to-end optimization of IterSynth-style search agents. 5.3 RDPO End-to-End Optimizes Role-Specific Performance To examine whether the gains of IterSynth-8B genuinely stem from the joint training of both cognitive roles, we ablate each role in isolation by replacing it with the untrained Qwen3-8B base model while keeping the other role at the trained IterSynth checkpoint. Table 3 reports the resulting performance on three long-horizon benchmarks. The results show that RDPO end-to-end optimizes both role-specific behaviors in IterSynth. Replacing either role with the untrained base model causes consistent and substantial drops across all benchmarks: the average score decreases by 17.4%17.4\% when the Synthesizer is replaced and by 41.1%41.1\% when the Planner is replaced. Since the role prompts, workflow, and workspace construction are unchanged, these drops indicate that the gains do not come from the IterSynth structure alone; rather, RDPO improves the functional competence of both roles within the long-horizon search process. The larger degradation from replacing the Planner suggests that planning is the more demanding role. A weak Planner produces poor sub-queries that corrupt the evidence collected throughout the trajectory, leaving limited room for the Synthesizer to recover. When only the Synthesizer is weakened, by contrast, the trained Planner can partially compensate by issuing targeted follow-up searches over the imperfect workspace. These results support the design motivation of RDPO: different roles contribute differently to long-horizon behavior, and decoupled credit assignment is necessary to jointly optimize them with appropriate training signals. 6 Conclusion We presented IterSynth, a role-decoupled and summary-based paradigm for long-horizon deep search. IterSynth alternates between a Planner and a Synthesizer within one shared LLM policy, using an evolving summary as the persistent search state to reduce capability coupling and context accumulation. We further introduced Role-Decoupled Policy Optimization (RDPO), which combines outcome rewards with turn-level rubric rewards and computes role-specific advantages for cleaner credit assignment. 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(2026) Yuhang Zhou, Kai Zheng, Qiguang Chen, Mengkang Hu, Qingfeng Sun, Can Xu, and Jingjing Chen. Offseeker: Online reinforcement learning is not all you need for deep research agents, 2026. URL https://arxiv.org/abs/2601.18467. Appendix A Artifact Statement This section consolidates information regarding the scientific artifacts used and produced in this work, in accordance with the ARR Responsible NLP Research checklist. A.1 Licenses and Terms of Use All existing artifacts used in this work are obtained from their official releases and are used in compliance with their respective licenses and terms of use. The backbone model Qwen3-8B (Yang et al., 2025) is released under the Apache 2.0 license; the trajectory-synthesis model Qwen3.5-397B-A17B is accessed under its official model license. The public deep-search datasets DeepDive (Lu et al., 2025), DeepForge (Petrik & Bambach, 2024), and REDSearcher (Chu et al., 2026) are used under the licenses declared in their original releases (Apache 2.0 or equivalent permissive licenses for research use). Evaluation benchmarks, including BrowseComp (Wei et al., 2025), BrowseComp-ZH (Zhou et al., 2025), GAIA (Mialon et al., 2023), and Xbench-DeepSearch (Xbench-Team, 2025), are used strictly for research evaluation in accordance with their stated terms. The training frameworks LLaMA-Factory (Zheng et al., 2024) and verl (Sheng et al., 2024) are open-source projects released under permissive licenses. The artifacts produced in this work, including the IterSynth-8B checkpoint, the curated SFT corpus, and the RDPO training pipeline, will be released under a permissive research license, consistent with the licenses of the underlying base model and source datasets. A.2 Consistency with Intended Use All existing artifacts are used strictly within the scope of their original intended use. Public deep-search datasets and evaluation benchmarks are used solely for non-commercial research on long-horizon search agents, which matches the research-only access conditions under which they were released. The base models and training frameworks are likewise applied within their declared scope, i.e., research and development of language-model-based systems. The derivative artifacts produced in this work, including the IterSynth-8B checkpoint and the curated training data, are intended exclusively for research on deep search agents and are not designed for, nor recommended for, deployment in user-facing production systems without additional safety, privacy, and quality assurance. A.3 Documentation of Artifacts The training data covers long-horizon question answering that requires multi-step search, web navigation, cross-document reasoning, and evidence synthesis. The questions span both English and Chinese, with English originating primarily from the public DeepDive and DeepForge sources, and Chinese contributed by REDSearcher and the synthesized real-world instances. Topical coverage is broad and includes general world knowledge, scientific and technical concepts, geographic and biographical facts, current events that are already public, and other open-domain information needs. The dataset is not designed to characterize any specific demographic group, and no demographic attributes (e.g., gender, race, religion) are used as supervision signals or filtering criteria. The evaluation benchmarks (BrowseComp, BrowseComp-ZH, GAIA, Xbench-DeepSearch) are used as released by their respective authors; readers are referred to the original papers for benchmark-level documentation, including construction protocols and intended scope. A.4 Data Statistics The SFT corpus contains approximately 1010K trajectories retained after the multi-stage filtering pipeline described in Appendix B.3. Each trajectory contains on average 4.374.37 Planner–Synthesizer iterations, and each trajectory is unrolled into per-turn training samples in which every Planner response and every Synthesizer response constitutes an independent supervised target. This yields approximately 10,000×4.37×2≈87.410,000× 4.37× 2≈ 87.4K role-conditioned training samples used during full-parameter SFT. The RL training set is constructed by performing five independent rollouts per SFT query and retaining only queries solved correctly in exactly one or two of the five attempts, targeting the moderate-difficulty regime. For evaluation, we report results on five long-horizon benchmarks: BrowseComp (1,2661,266 questions in the public release), BrowseComp-ZH (289289 questions), GAIA (text-only validation split, 103103 questions), and Xbench-DeepSearch (the 2505 and 2510 splits, each containing 100100 questions). Following the original protocols of each benchmark, we use the released test/validation splits for evaluation and do not use any benchmark data during training. The training/evaluation partition is therefore strictly disjoint at the benchmark level. Appendix B Implementation Details In this section, comprehensive implementation details of the proposed IterSynth framework are provided, encompassing the algorithmic pipeline, tool environment, and training configurations. B.1 Algorithmic Framework of IterSynth Unlike traditional mono-contextual agents, the execution of IterSynth proceeds through a strict alternation between two decoupled roles. The detailed operational mechanism is formally summarized in Algorithm 1. At each round t, the pipeline strictly bounds the context window. The Planner policy processes the compressed state st=(q,Mt)s_t=(q,M_t) to determine the subsequent analytical action. Once the environment returns a raw observation oto_t, the Synthesizer policy extracts task-relevant information, discards exploratory noise, and integrates the findings into an updated global memory Mt+1M_t+1. The trajectory history is explicitly truncated at each step, ensuring that the model is insulated from an infinitely accumulating context. Algorithm 1 The IterSynth Paradigm 1: Question q, Shared policy πθ _$θ$, Environment ℰE, Max rounds TmaxT_ 2: Final answer to q 3: Initialize: M0←∅,t←0M_0← ,t← 0 4: while t<Tmaxt<T_ do 5: st←(q,Mt)s_t←(q,M_t) ⊳ Construct bounded context 6: at←πθ(st)a_t← _$θ$(s_t) ⊳ Planner analytical step 7: if ata_t is Answer then 8: break ⊳ Termination signal 9: end if 10: ot←ℰ(at)o_t (a_t) ⊳ Environment execution 11: Mt+1←πθ(q,Mt,at,ot)M_t+1← _$θ$(q,M_t,a_t,o_t) ⊳ Synthesis & Denoising 12: t←t+1t← t+1 ⊳ Workspace reconstruction 13: end while 14: return final answer parsed from ata_t B.2 Tool Environment To facilitate long-horizon information seeking, the environment is equipped with two primary tools: • Search Engine: An interface to Google Search through the SerpAPI. It accepts multiple queries simultaneously and returns the title, URL, and text snippet of each of the top-10 results. • Web Browser: This tool facilitates granular information extraction from specified URLs via query-conditioned summarization. By conditioning the process on both the target URL and a sub-query derived from the main problem, the system ensures that the retrieved content remains contextually focused. The utility supports both HTML and PDF formats, utilizing Jina Reader for robust document parsing. Raw content is subsequently processed by a Qwen3-8B model to generate concise summaries that directly address the informational requirements of the agent. To keep the cost of large-scale rollouts tractable, the tool environment is configured differently between training and evaluation. During RL training, all tool calls are served by an offline cached search service: queries issued by the policy are matched against a pre-collected snapshot of search and webpage contents, so that no live API requests are made during training and the per-rollout cost remains predictable. At evaluation time, the same tool interface is connected to the live environment described above, ensuring that the reported numbers reflect the real-world deep search setting rather than the cached training distribution. B.3 Training Configurations wraptable [18]r Hyperparameters and training resources used during the Supervised Fine-Tuning (SFT) phase. Hyperparameter Value Framework LLaMA-Factory Hardware 32×32× H20 (9696 GB) Training Time ∼ 2424 h Learning Rate 1×10−51× 10^-5 Batch Size 512 Number of Epochs 4 Chat Template Qwen Max Context Length 32,768 Warmup Ratio 0.05 LR Scheduler Cosine Training Data. The SFT training corpus is constructed by aggregating publicly available deep-search datasets with curated synthetic real-world instances. The public sources include DeepDive (Lu et al., 2025), DeepForge (Petrik & Bambach, 2024), and REDSearcher (Chu et al., 2026), which together cover a broad range of long-horizon, multi-hop, and tool-augmented question-answering tasks. To complement these sources with more realistic deployment scenarios, we additionally curate synthetic real-world questions that target topical and reasoning-pattern coverage not well represented in the public mixture. Starting from this question pool, we synthesize one Planner–Synthesizer trajectory per question by prompting the Qwen3.5-397B-A17B (Yang et al., 2025) foundation model to roll out the IterSynth dual-role loop in a live search environment, so that every Planner action is grounded in real retrieval results rather than imagined evidence. After multi-stage filtering (described below), 10,00010,000 high-quality trajectories are retained, with an average length of 4.374.37 Planner–Synthesizer iterations per trajectory. Because each trajectory is subsequently unrolled into per-turn training samples in which every Planner and every Synthesizer response constitutes an independent supervised target, the final SFT sample set contains approximately 10,000×4.37×2≈87.410,000× 4.37× 2≈ 87.4K role-conditioned samples. Trajectory Filtering Pipeline. The raw synthesized trajectories are passed through a filtering pipeline that enforces both behavioral validity and semantic faithfulness: • Format and tool-call validation. Trajectories with malformed JSON, missing role tags, or invalid tool-call schemas are first repaired when the corruption is purely syntactic, and otherwise discarded. Calls whose arguments do not match the declared search interface are also removed. • Outcome correctness gating. Finally, only trajectories whose terminal answer is verifiably correct against the gold reference are retained, ensuring that the supervised demonstrations represent successful long-horizon search rather than merely well-formatted exploration. Supervised Fine-Tuning (SFT). The single Qwen3-8B (Yang et al., 2025) backbone is optimized to perform both the Planner and the Synthesizer roles purely through role-specific prompts, with no role-specific parameters or adapters introduced. We perform full-parameter SFT over the per-turn sample set described above using the LLaMA-Factory (Zheng et al., 2024) framework. For each training instance, the loss is computed only on the role response (the Planner action or the Synthesizer summary) conditioned on the user question, the reconstructed workspace at the corresponding turn, and the role-specific prompt; tool-call results returned by the environment are masked out from the loss to prevent the model from learning to imitate retrieval content. SFT is run on 3232 NVIDIA H20 GPUs (9696 GB each) and completes in approximately 2424 hours. Detailed training hyperparameters, including optimizer, learning-rate schedule, sequence length, and batch composition, are provided in Table B.3. Role-Decoupled Policy Optimization. To construct the reinforcement learning dataset, queries from the SFT phase are subjected to five independent rollouts utilizing the SFT checkpoint. Only queries yielding a correct final answer in exactly one or two out of the five attempts are retained, yielding a final RL training set of approximately 1,0001,000 queries. This selection criterion deliberately targets the “zone of proximal development”, ensuring that the policy receives informative gradient signals from instances of moderate difficulty. The reinforcement learning phase is conducted with the Role-Decoupled Policy Optimization (RDPO) algorithm and role-decoupled rubric rewards, implemented on top of the verl (Sheng et al., 2024) framework, which provides distributed rollout collection and policy updates over multi-turn agent trajectories. RL training is run on 6464 NVIDIA H20 GPUs (9696 GB each) and completes in approximately 9090 hours. Detailed hyperparameters are summarized in Table B.3. wraptable r Hyperparameters utilized during the Role-Decoupled Policy Optimization phase. Hyperparameter Value Learning Rate 1×10−61× 10^-6 Batch Size 32 Group Size Per Question 8 Temperature 1.0 Top-p 0.95 KL Loss Coefficient (λ) 0.001 Entropy Coefficient 0.0 Max Context Length 32,768 Max Rounds (TmaxT_ ) 30 α 0.5 Maximum Round Settings. We adopt stage-specific maximum-round limits TmaxT_ to balance training efficiency with inference flexibility. During both SFT trajectory synthesis and RL training, we constrain Tmax=30T_ \!=\!30 Planner–Synthesizer iterations. This budget is sufficient to expose the model to genuinely long-horizon exploration while keeping per-trajectory cost bounded, and it implicitly encourages the policy to develop concise rather than exhaustive search strategies. At evaluation time, we relax TmaxT_ to 5050 on all benchmarks, providing additional exploration budget for the hardest long-horizon queries while preserving the iterative behavior learned during training. Appendix C Rubric Construction and Reward Model Setup This appendix provides the full procedure for constructing the role-specific rubrics used in RDPO and the prompt template employed by the reward model at training time. C.1 Rubric Construction Pipeline The rubrics for the Planner and the Synthesizer are not handcrafted but distilled from contrastive trajectory pairs through a three-stage pipeline. Stage 1: Contrastive Trajectory Collection. Starting from the SFT-initialized policy described in Section 3.2.1, we run rollouts on a held-out set of long-horizon deep search queries and collect 2020 positive–negative trajectory pairs. Within each pair, the two trajectories address the identical query, but the positive trajectory reaches the correct final answer whereas the negative trajectory fails. Both trajectories are recorded as complete sequences of alternating Planner and Synthesizer sub-steps, so that role-specific behaviors can be examined independently. Stage 2: Per-Pair Rubric Induction. For each contrastive pair, Claude-4.6-Sonnet is prompted to analyze the divergence between the positive and the negative trajectory and to produce, separately for the Planner and the Synthesizer, a list of evaluative criteria that distinguishes successful from unsuccessful behaviors. The two roles are processed in independent prompts so that planning-oriented criteria (for example, the precision of sub-query formulation or the timing of termination) are not conflated with synthesis-oriented criteria (for example, the faithfulness of evidence integration or the suppression of off-topic information). This stage yields 2020 candidate rubric lists per role. Stage 3: Rubric Consolidation. The 2020 role-specific rubric lists are then provided to Claude-4.6-Sonnet, which merges semantically overlapping criteria, removes redundant items, and consolidates the result into a single unified rubric per role. To ensure that the reward signal remains compact and trainable, the consolidation prompt explicitly caps the final rubric at five items per role. The resulting two rubrics, one for the Planner and one for the Synthesizer, are then frozen and used throughout the RL stage. C.2 Reward Modeling at Training Time During RL, the frozen role-specific rubrics are scored online by an LLM judge based on Gemini-2.5-flash-lite, which is selected for its favorable balance between scoring quality and inference cost. At every turn t within each rollout, the judge is invoked twice, once with the Planner rubric over the Planner output dtPd_t^P and once with the Synthesizer rubric over the Synthesizer output dtSd_t^S, producing the per-role rubric rewards rrubric,i,tPr_rubric,i,t^P and rrubric,i,tSr_rubric,i,t^S that enter the composite reward defined in Section 3.2.2. Appendix D Feature-Level Comparison with Prior Agent Frameworks wraptable [15]r Feature-level comparison across four architectural dimensions. IterSynth is the only single-policy method in this comparison that jointly achieves role decoupling and workspace reconstruction, resulting in a structurally bounded per-iteration context. Method Single Role Wksp. Bdd. Policy Decoupl. Reconstr. Ctx. OpenSeeker-30B (Du et al., 2026) ✓ – – – IterResearch-30B (Chen et al., 2026a) ✓ – ✓ ✓ WebSailor-V2-30B (Li et al., 2025a) ✓ – – – OffSeeker-8B (Zhou et al., 2026) ✓ – – – WebExplorer-8B (Liu et al., 2025) ✓ – – – AgentCPM-4B (Chen et al., 2026b) ✓ – – ✓ IterSynth-8B ✓ ✓ ✓ ✓ To make explicit how IterSynth relates to prior deep-search agent frameworks discussed in Section 2.2, Table D compares IterSynth against representative baselines from Table 1 along four architectural dimensions: whether the agent uses a single shared policy (as opposed to multiple independently parameterized models), whether it decouples cognitive roles (e.g., planning vs. synthesis), whether it reconstructs its working context rather than appending to an ever-growing history, and whether its per-iteration context is structurally bounded as a result. This comparison highlights two structural gaps in prior work that motivate IterSynth’s design. First, existing single-policy agents (OpenSeeker, OffSeeker, WebExplorer, AgentCPM-Explore) do not incorporate role decoupling: they treat planning, evidence use, and answer synthesis as a single undifferentiated decision process, without role-specific prompts, action spaces, or credit assignment. Second, among all compared methods, only IterResearch employs workspace reconstruction to bound context, but it does so within a single undifferentiated role, so its MDP formulation cannot benefit from the role-specific optimization that RDPO provides. IterSynth is therefore the only method that jointly combines a single shared policy, explicit role decoupling, and iterative workspace reconstruction, and Appendix F.3 shows that this combination translates into concretely fewer search rounds and a substantially lower context-exhaustion rate relative to both dimensions considered in isolation. Appendix E Controlled Comparison: Architecture vs. Trajectory Quality wraptable [11]r Controlled comparison between ReAct-style and IterSynth-style trajectories under identical data scale, teacher model, and training pipeline. Method BC BCzh GAIA Xb05 Xb10 Avg ReAct-SFT 17.5 32.5 43.2 38.0 32.0 32.6 ReAct-RL 19.2 34.3 45.8 50.0 41.0 38.1 IterSynth-SFT 23.7 50.1 47.6 61.0 38.0 44.1 IterSynth-RDPO 30.9 55.4 55.3 66.0 46.0 50.7 A possible confound in our main comparisons is that IterSynth’s gains might stem primarily from the higher quality of its teacher-generated SFT trajectories (synthesized via the dual-role rollout described in Appendix B.3), rather than from the IterSynth architecture and the RDPO algorithm themselves. To disentangle these factors, we train a controlled ReAct baseline under an identical data scale and training pipeline: the same underlying questions are used to synthesize single-role ReAct-style trajectories (rather than Planner–Synthesizer trajectories) with the same teacher model, and the resulting corpus is used for SFT and subsequent RL with an identical (outcome-only) reward for the RL stage. As shown in Table E, under identical SFT training, IterSynth-SFT outperforms ReAct-SFT by +11.5+11.5 points on average, showing that the architectural design (role decoupling and workspace reconstruction) provides a substantial benefit independent of the RL algorithm used afterward. When both recipes are subsequently trained with RL, IterSynth-RDPO still exceeds ReAct-RL by +12.6+12.6 points, and the RL-stage gain over the corresponding SFT baseline is larger for IterSynth (+6.6+6.6) than for ReAct (+5.5+5.5), suggesting that RDPO’s role-decoupled credit assignment is particularly effective precisely because IterSynth’s architecture provides well-separated Planner and Synthesizer sub-steps for it to act on. Together, these controlled comparisons indicate that both the IterSynth architecture and the RDPO training algorithm contribute meaningfully to the final performance, beyond what could be explained by trajectory quality alone. Appendix F Parameter Sharing, Efficiency, and Failure Modes This section provides the full experimental detail underlying the summary given in Section . F.1 Shared Parameters Are a Net Gain, Not a Trade-off wraptable [12]r Shared vs. independent parameters under the same total parameter budget. SFT-Independent uses two separately fine-tuned 8B models, one per role. Method BC BCzh GAIA Xb05 Xb10 Avg SFT-Shared 23.7 50.1 47.6 61.0 38.0 44.1 SFT-Independent 21.2 48.4 43.5 55.0 35.0 40.6 Δ +2.5 +1.7 +4.1 +6.0 +3.0 +3.5 A natural concern with IterSynth’s single-policy design is that forcing the Planner and the Synthesizer to share parameters may induce capacity competition, in which the two roles interfere with each other rather than benefiting from joint training. To test this, we train an otherwise identical dual-model variant, SFT-Independent, in which the Planner and the Synthesizer are two separate copies of Qwen3-8B, each fine-tuned only on its own role-specific portion of the SFT corpus, and compare it against our default shared-parameter model, SFT-Shared, under an identical total parameter budget (i.e., the independent variant uses two 8B models rather than one). As shown in Table F.1, the shared-parameter model consistently outperforms the independent-parameter model by +3.5+3.5 points on average, with the largest gains on Xbench-2505 (+6.0+6.0) and GAIA (+4.1+4.1). This indicates that, rather than causing capacity competition, parameter sharing enables beneficial knowledge transfer: the Planner and the Synthesizer draw on common underlying reasoning primitives, such as evidence evaluation and relevance judgment, and joint training over both roles’ data allows the shared policy to develop more robust representations than either role could learn from its own half of the corpus alone. The independent variant, in contrast, suffers from effective data fragmentation, since each separately trained model only observes role-specific supervision. We therefore conclude that the single-policy design of IterSynth is a net gain rather than a trade-off: it simultaneously halves deployment cost relative to a dual-model system and improves accuracy. F.2 Efficiency and Cost Analysis Because RDPO invokes an LLM judge for both roles at every turn, and because IterSynth itself performs two role-conditioned forward passes per iteration, a natural question is whether these gains come at a disproportionate training or inference cost relative to outcome-only RL and to larger trained agents. We quantify both costs directly. Training cost of the rubric judge. Trajectories that terminate early due to format errors, search errors, or exceeding the maximum round budget do not require judge scoring, which substantially reduces the number of LLM calls in practice. Under our training configuration (batch size 3232, group size 88), approximately 7070–9090 valid trajectories per step require rubric evaluation; with an average of ∼ 8 Planner–Synthesizer iterations per trajectory, this yields roughly 1,1201,120–1,4401,440 judge calls per training step, or approximately 128128K calls and 1.921.92B tokens over the full 100100-step run. At a representative API price of $0.1 per million input tokens, this amounts to an estimated additional cost of ∼ $192 for the entire RDPO stage, i.e., roughly $54.9 per point of accuracy gained over outcome-only GRPO (see Table 3). This is a small fraction of the underlying GPU compute cost of RL training itself. wraptable [8]r Wall-clock inference time comparison between single-policy and dual-model deployment on identical total hardware. Deployment BCzh Xb05 Overhead Single-model (8 GPUs) 204.7 min 97.2 min – Dual-model (4+4 GPUs) 231.6 min 111.7 min +13–15% Inference cost of the single-policy design. We compare wall-clock inference time for IterSynth-8B under two deployment configurations on identical hardware (8 GPUs): a single-model deployment, where one set of weights serves both roles, and a hypothetical dual-model deployment, where the Planner and the Synthesizer are hosted as two separate model replicas (4+4 GPUs). As shown in Table F.2, the single-policy design incurs 1313–15%15\% less inference time than an equivalently-resourced dual-model deployment, since splitting the same hardware budget across two separate models reduces the effective batching efficiency available to each. Combined with the accuracy advantage established in Appendix F.1, this confirms that IterSynth’s shared-parameter architecture is preferable to a dual-model alternative on both efficiency and accuracy grounds. wraptable [9]r End-to-end inference time comparison against a 30B-scale agent on identical hardware. Model BCzh (min) Xb05 (min) Avg. Acc. Tongyi-DR-30B 312.5 156.8 58.2% IterSynth-8B 204.7 97.2 50.7% IterSynth-8B versus 30B-scale agents. We additionally benchmark end-to-end wall-clock inference time of IterSynth-8B against Tongyi-DR-30B on identical hardware. IterSynth-8B is 1.51.5–1.6×1.6× faster than Tongyi-DR-30B while reaching roughly 87%87\% of its average accuracy with 3.75×3.75× fewer parameters, and it still exceeds several other 3030B-scale agents reported in Table 1 (e.g., OpenSeeker-30B-SFT, AgentFold-30B-A3B). This favorable accuracy-per-compute trade-off makes IterSynth particularly attractive for resource-constrained deployments. F.3 Trajectory-Level Statistics wraptable [11]r Trajectory-level statistics comparing ReAct, IterResearch, and IterSynth. Metric ReAct IterRes. IterSynth Avg. search rounds 12+ 8–10 7–8 Avg. context length 64K+ (unbdd.) ∼ 32K ∼ 32K (bdd.) Context-exhaustion rate 59% <<5% <<5% Not-attempted rate Moderate Low Low Avg. search calls/query 15+ 10–12 8–10 To further characterize why IterSynth avoids the context-exhaustion failure mode analyzed in Appendix H, we compare trajectory-level statistics across ReAct, IterResearch, and IterSynth, all instantiated with comparable backbones and evaluated on the same long-horizon benchmarks. IterSynth converges in fewer rounds and fewer search calls than both ReAct and IterResearch, while its context-exhaustion rate remains below 5%5\%, consistent with the 59%59\% non-completion rate measured for ReAct in Appendix H. This indicates that workspace reconstruction is not merely a safeguard against context overflow, but also promotes more efficient information gathering: rather than continuing to search until an arbitrary budget is exhausted, IterSynth’s bounded, reconstructed context encourages the Planner to terminate once sufficient evidence has been consolidated by the Synthesizer. F.4 Qualitative Failure Analysis To better understand the residual error modes of IterSynth-8B, we manually inspect a sample of incorrect trajectories on GAIA and BrowseComp and identify two recurring failure patterns. Summary information loss (GAIA). On GAIA questions that require chaining several disparate pieces of evidence, the Synthesizer’s global summary occasionally omits the specific relational link between two otherwise correctly retrieved facts (e.g., recording two entities and their individual attributes without explicitly recording the relationship that connects them). When the Planner later commits to a final answer based on this summary, the missing connective information can lead to an incorrect conclusion even though all the necessary facts were retrieved earlier in the trajectory. This suggests that future work could benefit from more structured summary representations that explicitly preserve entity relations rather than only flat factual statements. Incorrect evidence consolidation (BrowseComp). On BrowseComp questions where the search engine returns conflicting results, we observe cases in which the Synthesizer consolidates a superficially authoritative but factually incorrect source into the summary, and this early commitment then biases how subsequent, correct evidence is interpreted in later rounds. This resembles a confirmation-bias failure mode: once an initial (incorrect) claim enters the persistent memory, later rounds tend to search for and integrate corroborating rather than contradicting evidence. This highlights a concrete direction for future work: incorporating an explicit contradiction-detection step into the Synthesizer’s update rule, so that newly retrieved evidence that conflicts with the existing summary triggers targeted re-verification rather than passive accumulation. Appendix G Ablation experiments Effect of the Rubric Reward Coefficient α. The composite reward in RDPO, ri,tρ=racc,i+α⋅rrubric,i,tρr_i,t^ρ=r_acc,i+α· r_rubric,i,t^ρ, balances the globally broadcast outcome signal against the role-specific turn-level rubric signal. To examine the sensitivity of IterSynth-RDPO to this trade-off, we compare three settings, α∈0.3,0.5,0.7α\!∈\!\0.3,0.5,0.7\, while keeping all other components identical to the main configuration; α=0.5α\!=\!0.5 is the value adopted by IterSynth-RDPO-8B in the main results. As reported in Table G, α=0.5α\!=\!0.5 consistently outperforms α=0.3α\!=\!0.3 on every benchmark, with absolute gains of +3.4+3.4 on BrowseComp, +6.6+6.6 on BrowseComp-ZH, +4.8+4.8 on GAIA, +3.0+3.0 on Xbench-2505, and +3.0+3.0 on Xbench-2510, corresponding to an average improvement of +4.2+4.2 points. Increasing α further to 0.70.7 instead decreases the average score to 49.049.0, indicating a non-monotonic, inverted-U relationship between α and downstream accuracy. wraptable [12]r Ablation on the rubric-reward coefficient α in RDPO. α=0.5α\!=\!0.5 is the value used by IterSynth-8B in the main results. α BC BC-zh GAIA Xb-05 Xb-10 Avg 0.30.3 27.5 48.8 50.5 63.0 43.0 46.6 0.50.5 30.9 55.4 55.3 66.0 46.0 50.7 0.70.7 29.1 53.8 53.2 63.0 45.0 49.0 Δ (0.5 v. 0.3) +3.4 +6.6 +4.8 +3.0 +3.0 +4.1 Two observations follow from this comparison. First, the consistency of the improvement across five benchmarks of distinct character, ranging from English exploration-heavy tasks (BrowseComp) to Chinese long-horizon search (BrowseComp-ZH) and to mixed reasoning–retrieval benchmarks (GAIA, Xbench), indicates that the trade-off encoded by α is not a benchmark-specific artifact but a stable property of how RDPO weighs the two reward components. When α is too small, the per-turn rubric signal is dominated by the broadcast outcome term and the role-specific gradient becomes too weak to materially shape intermediate Planner and Synthesizer behaviors; the optimization then degenerates toward outcome-only RL, losing much of the credit assignment benefit that motivates RDPO in the first place. When α is too large (0.70.7), the intermediate rubric signal begins to dominate the terminal outcome reward, which can bias the policy toward locally rewarded, rubric-pleasing behaviors at the expense of ultimate task completion; this over-weighting of turn-level signal likely explains the observed drop relative to α=0.5α\!=\!0.5. Second, the gain is most pronounced on BrowseComp-ZH (+6.6+6.6) and GAIA (+4.8+4.8), the two benchmarks whose trajectories tend to be longer and whose intermediate decisions, such as sub-query refinement and faithful evidence integration, have the largest cumulative effect on the final answer. On these tasks, a larger α amplifies the role-decoupled feedback exactly where it matters most: it tells the Planner whether each individual sub-query is well posed and tells the Synthesizer whether each summary update faithfully integrates the new evidence, rather than only attributing credit to the trajectory as a whole. By contrast, the relative gain is smaller on Xbench-2505 and Xbench-2510, where shorter trajectories leave less room for differentiated turn-level shaping. Overall, this analysis indicates that an appropriately weighted rubric term, with α=0.5α\!=\!0.5 in our configuration, is necessary for RDPO to translate its structural design into reliable empirical gains; we therefore adopt α=0.5α\!=\!0.5 in all main experiments. Robustness to the Choice of Judge Model. Since RDPO relies on an LLM judge to score the role-specific rubrics, a natural concern is whether the resulting gains are an artifact of the particular judge model (Gemini-2.5-flash-lite) used in our main experiments. To assess this, we swap in three alternative judge models, DeepSeek-V3.1, GPT-4o-mini, and Gemini-2.0-flash, and compare their average rubric scores on a fixed sample of 200200 Planner and Synthesizer outputs, as well as the resulting downstream RDPO performance. wraptable [12]r Average rubric scores (1–5 scale) assigned by four different judge models on a fixed sample of 200200 Planner/Synthesizer outputs. Judge Model Planner Synth. Overall DeepSeek-V3.1 3.42 3.58 3.50 GPT-4o-mini 3.38 3.51 3.45 Gemini-2.5-flash-lite (main) 3.45 3.62 3.54 Gemini-2.0-flash 3.40 3.55 3.48 The average rubric scores across the four judge models are remarkably consistent, ranging only from 3.453.45 to 3.543.54 overall (±0.09± 0.09). Re-running RDPO training with each alternative judge yields final average benchmark performance within ±1.0± 1.0 point of the main result (50.750.7), and the relative ordering RDPO >> GRPO >> SFT is preserved in every case. We additionally manually inspect a sample of judged trajectories and confirm that rubric scores agree with human judgment: trajectories rated highly by the judge consistently exhibit more precise sub-query formulation and more faithful evidence integration upon human review. Taken together, these results indicate that RDPO’s benefits stem from the structural property of providing dense, role-specific, turn-level feedback, rather than from idiosyncrasies of any single judge model. Appendix H Context Overflow under the ReAct Paradigm To empirically substantiate the context-suffocation bottleneck of mono-contextual agents discussed in Section 1, we analyze how often a strong frontier backbone, when driven by the standard ReAct paradigm, fails to complete a deep search trajectory before exhausting its context window. We run two backbones, DeepSeek-V3.1 and Claude-Sonnet-4.6, under an identical ReAct deep search agent on three long-horizon benchmarks: xBench-2510, a randomly sampled 100100-question subset of BrowseComp-ZH (BC-zh-100100), and a randomly sampled 100100-question subset of BrowseComp (BC-100100). For all runs the maximum context length is fixed at 6464K tokens, which is generous relative to typical deep-search deployments and ensures that any observed non-completion reflects the trajectory itself outgrowing a sizeable budget rather than an artificially tight cap. For each query, we classify the trajectory into one of three mutually exclusive outcomes: Succeeded, where the trajectory terminates within the 6464K budget and produces the gold answer; Failed, where it terminates within budget but with an incorrect or off-topic answer; and Not completed, where it hits the 6464K maximum context length before any final answer is emitted. The corresponding distribution is reported in Figure H. Non-completion is a systemic failure mode, not an outlier. Across all three benchmarks and both backbones, the Not-completed ratio is consistently non-trivial despite the 6464K budget. Even on the relatively shorter xBench-2510, 27%27\% of DeepSeek-V3.1 trajectories and 30%30\% of Claude-Sonnet-4.6 trajectories fail to terminate before the context window is exhausted; on BC-zh-100100 these numbers grow to 31%31\% and 34%34\%, respectively. The two backbones differ noticeably in their final accuracy (3636 vs. 4242 on xBench-2510, 4141 vs. 4747 on BC-zh-100100), yet their non-completion rates remain within a few percentage points of each other on the same benchmark. This pattern indicates that context exhaustion is a property of the ReAct paradigm itself rather than of any particular model: simply replacing the backbone with a stronger frontier system shifts the balance between Succeeded and Failed trajectories, but does not eliminate the underlying bottleneck. Non-completion scales with task horizon and dominates on the hardest benchmark. Comparing the three benchmarks reveals a clear monotonic trend: the Not-completed ratio grows from ∼ 27%27\%–30%30\% on xBench-2510, to ∼ 31%31\%–34%34\% on BC-zh-100100, to ∼ 59%59\%–65%65\% on BC-100100, tracking the increasing search depth and breadth typically required by these tasks. On BC-100100, non-completion becomes the dominant outcome, accounting for roughly six to seven of every ten queries, while Succeeded trajectories collapse to only 7%7\% for DeepSeek-V3.1 and 14%14\% for Claude-Sonnet-4.6. In other words, on the most exploration-intensive benchmark the principal obstacle is no longer reasoning quality but the inability of a single, ever-expanding context to hold the trajectory needed to reach an answer at all, even when up to 6464K tokens are available. wrapfigure [22]r Trajectory-level outcome distribution of the ReAct paradigm with DeepSeek-V3.1 and Claude-Sonnet-4.6 on three long-horizon benchmarks, evaluated under a 6464K maximum context length. Each pair of bars decomposes trajectories into Succeeded (terminated within budget with the gold answer), Failed (terminated within budget but incorrect), and Not completed (the 6464K maximum context length is reached before any final answer is emitted). Context exhaustion is a substantial failure mode on all three benchmarks and grows monotonically with task horizon, exceeding 59%59\% on BrowseComp-100100. Implication for IterSynth. These observations directly motivate the workspace-reconstruction mechanism in IterSynth (Section 3.1.2). By alternating between a Planner that operates on a compact, persistent summary and a Synthesizer that periodically rebuilds the workspace, IterSynth structurally bounds the per-iteration context regardless of the search horizon, and therefore avoids the non-completion regime that consumes a large fraction of ReAct trajectories under the same 6464K budget. Crucially, the gains observed in our main results on BrowseComp and BrowseComp-ZH are largest precisely on the benchmarks where Figure H reports the highest ReAct non-completion rates, providing complementary evidence that the proposed paradigm directly targets the empirically dominant failure mode of mono-contextual long-horizon agents. Appendix I Training Dynamics This section provides a fine-grained analysis of how the IterSynth policy evolves during RDPO optimization, complementing the end-to-end benchmark results reported in the main paper. The analysis is organized along three axes that jointly characterize agent behavior: search behavior, capturing how the policy interacts with the external environment; training-side optimization signals, characterizing the internal learning dynamics; and inference-side output statistics, summarizing the structural properties of generated responses. Figure 3 visualizes all six diagnostic curves grouped by axis. All curves are recorded at the per-step granularity of the RDPO update; raw measurements are shown as light lines and an EMA-smoothed estimate is overlaid as a solid line for visual clarity. (a) IterSynth avg rounds. (b) Training trajectory accuracy. (c) Validation trajectory accuracy. (d) Training entropy. (e) Rubric score on correct trajectories. (f) Mean response length per role turn. Figure 3: RDPO training dynamics across three diagnostic axes. The top row reports search-related behavior: (a) average Planner–Synthesizer iterations per trajectory, (b) trajectory-level accuracy on training rollouts, and (c) trajectory-level accuracy on a held-out validation set. The bottom row reports training-side optimization signals, including (d) training entropy and (e) the average rubric score evaluated on correct trajectories, together with (f) inference-side mean response length averaged across all Planner and Synthesizer turns. I.1 Search-Related Behavior Tool-use intensity. The average number of Planner–Synthesizer iterations per trajectory, plotted in Figure 3(a), serves as a direct proxy for tool-use intensity. The curve exhibits a pronounced two-phase pattern. During the early phase (steps 00–4545), the average length grows rapidly from approximately 44 to a peak of around 1212 iterations, indicating that the policy initially responds to the introduction of outcome and rubric signals by issuing additional searches in order to fill information gaps. Beyond step 4545, the curve gradually contracts and stabilizes around 77–88 iterations, suggesting that the policy progressively learns to terminate more efficiently once a sufficient evidence base has been accumulated, rather than continuing to expand its tool budget without bound. Task completion rate. Figures 3(b) and 3(c) report the trajectory-level correctness on training rollouts and on a held-out validation set, respectively. Training accuracy increases from approximately 0.350.35 at the beginning of RDPO to roughly 0.530.53 at convergence, while validation accuracy improves from about 0.330.33 to 0.440.44. The two curves rise in close synchrony, with no apparent divergence between in-distribution and held-out performance, which suggests that the role-decoupled rubric signal does not induce reward hacking on the training distribution. The steepest validation gain (0.34→0.420.34→ 0.42, steps 4040–7070) coincides with the contraction phase of the average-rounds curve, supporting the interpretation that the policy increasingly converts exploration into reliable answers rather than into longer trajectories. I.2 Training-Side Optimization Signals Policy entropy. Figure 3(d) reports the per-step training entropy. Over the first ∼ 4040 steps, the entropy decreases sharply from 0.290.29 to approximately 0.2450.245, mirroring the rapid behavioral change observed in the average-rounds and training-accuracy curves over the same window: the policy commits to the dual-role search routine and prunes unproductive action distributions. From step ∼ 4545 onward, the entropy mildly recovers to a stable plateau around 0.250.25–0.260.26 rather than collapsing toward zero. This bounded entropy is desirable, as it indicates that the policy retains sufficient stochasticity to explore alternative search strategies on hard queries, a known prerequisite for stable long-horizon RL training. Reward signal. Although trajectory accuracy in Figure 3(b) already characterizes outcome-level optimization, RDPO additionally relies on a turn-level rubric reward whose dynamics warrant separate inspection. Figure 3(e) reports the average rubric score evaluated on the subset of correct trajectories, which isolates rubric quality from outcome quality. The score increases monotonically from ≈ 0.320.32 to ≈ 0.380.38 over the 120120 training steps, demonstrating that, even among trajectories that already obtain a terminal reward of racc=1r_acc=1, the rubric reward continues to drive meaningful improvements: the policy keeps refining intermediate Planner and Synthesizer behaviors, such as query precision and faithful summary updates, beyond what outcome rewards alone could express. This empirically supports the composite reward design introduced in Section 3.2.2. I.3 Inference-Side Output Statistics Response length. Figure 3(f) reports the mean response length, averaged over all Planner and Synthesizer turns. The curve grows from approximately 1,4251,425 tokens to a plateau around 1,5601,560–1,5801,580 tokens at step ∼ 4545 and remains essentially flat for the remainder of training. The early increase aligns temporally with the entropy decrease and the rise in average rounds, suggesting that the policy initially learns to produce more substantive role-specific outputs, namely more deliberate Planner reasoning and more comprehensive Synthesizer summaries. Importantly, response length does not continue to inflate during the second half of training, even as trajectory accuracy keeps improving. Combined with the contracting average-rounds curve, this observation indicates that later improvements arise from higher-quality rather than longer role outputs, which is consistent with role-decoupled credit assignment delivering targeted, structurally meaningful gradients rather than encouraging verbosity. Summary. Taken together, the three groups of dynamics yield a coherent picture of how RDPO shapes IterSynth: the policy first expands its search budget and per-turn output, and then progressively consolidates this exploration into shorter, more accurate, and more structurally faithful trajectories, while maintaining a healthy entropy level throughout. This evolution is consistent with the structural prediction of role-decoupled training discussed in Section 3.2.2. Appendix J Test-Time Scaling wraptable [10]r Effect of the proposed not-attempted resampling strategy at test time. Inference Strategy xBench-2510 BC BC-ZH IterSynth-8B 37.0 25.7 49.1 + Resample 46.0 30.9 55.4 Δ vs. baseline +9.0 +5.2 +6.3 We further examine whether the trained policy can convert additional inference compute into accuracy through a lightweight test-time scaling strategy. The strategy targets a specific failure mode observed during inference: when the answer produced at a Planner step is judged as not attempted, that is, the model commits to an answer that does not actually address the question, we resample the step exactly once. The resampled output may either choose to continue searching or to commit to a different answer, allowing the policy to recover from premature or off-topic terminations without modifying the training procedure. As reported in Table J, this single-shot resampling consistently improves IterSynth-8B over the no-resample baseline, with gains of +9.0+9.0 on xBench-2510, +5.2+5.2 on BrowseComp, and +6.3+6.3 on BrowseComp-ZH. The largest improvement appears on the most exploration-intensive benchmark, indicating that a non-trivial fraction of failures originates from premature termination rather than from a fundamental lack of capability, and that the policy trained with IterSynth and RDPO can smoothly translate a small amount of extra inference compute into reliable accuracy gains. Appendix K Case Study: Iterative Synthesis in Action To complement the quantitative analysis, we walk through a representative BrowseComp trajectory produced by IterSynth-8B, illustrating how the Planner and Synthesizer cooperate across iterations to gradually narrow down the answer. Examples of our IterSynth on a BrowseComp Question Question: An African author tragically passed away in a tragic road accident. As a child, he’d wanted to be a police officer. He lectured at a private university from 2018 until his death. In 2018, this author spoke about writing stories that have no sell by date in an interview. One of his books was selected to be a compulsory school reading in an African country in 2017. Which years did this author work as a probation officer? Answer: 1988–96 Round 1 (Planner) In the initial iteration, the Global Summary is empty. <think> Let me analyze the clues in this question to identify the African author: 1. African author 2. Died in a road accident (tragic) 3. As a child, wanted to be a police officer 4. Lectured at a private university from 2018 until his death 5. In 2018, spoke about writing stories that have no “sell by date” in an interview 6. One of his books was selected as compulsory school reading in an African country in 2017 7. Worked as a probation officer (the question asks which years) The question asks about years working as a probation officer. Let me search for this author. The clues about wanting to be a police officer as a child, working as a probation officer, being an African author who died in a road accident, and lecturing at a private university from 2018 are quite specific. Let me search for this. </think> <tool_call> “name”: “search”, “arguments”: “query”: [“African author died road accident lectured private university 2018 probation officer”, “African author road accident death book compulsory school reading 2017 probation officer”, “African author ‘no sell by date’ interview 2018 writing stories”] </tool_call> <tool_response> Query: “African author died road accident lectured private university 2018 probation officer” (8 documents) Document ID: doc_1 [webpage 1 begin] [webpage title] [PDF] FLOWSEARCHER: SYNTHESIZING MEMORY-GUIDED AGENTIC … [webpage source query] African author died road accident lectured private university 2018 probation officer [webpage content begin] The key identifying facts are: an African author who died in a road accident, lectured at a private university from 2018 until his death, had a book become. [webpage content end] [webpage 1 end] Document ID: doc_2 [webpage 2 begin] [webpage title] Prof Ken Walibora death: Public inquest into mystery … – Facebook … (As the content is too long, we will omit it here.) </tool_response> Round 1 (Synthesizer) <summary> 0) Latest Search Search Queries: 1. “African author died road accident lectured private university 2018 probation officer” – Intended to identify the author using multiple key traits. Yielded a mention of Ken Walibora (doc_2) but no direct detailed results. 2. “African author road accident death book compulsory school reading 2017 probation officer” – Intended to narrow down author by book/school reading clue. Yielded strong results: docs 9, 10, 11, 18 all point to Ken Walibora, with docs 9 and 18 explicitly stating he worked as a probation officer. 3. “African author ‘no sell by date’ interview 2018 writing stories” – Intended to confirm the 2018 interview quote. Yielded direct confirmation: doc_19 is an interview with Ken Walibora dated August 27, 2018, containing the exact quote about writing stories with “no sell by date.” Key Evidence Found: • The African author is Ken Walibora (full name: Ken Walibora Waliaula), a celebrated Kenyan/Swahili writer and journalist. • He died after being run over by a vehicle on Landhies Road in Nairobi (road accident) – April 2020. • He worked as a teacher and a probation officer before becoming a full-time writer and academic. • A 2018 interview (“African Literature through the Lens of Ken Walibora”) contains the exact quote: “I often strive to write stories that have no sell by date.” • The specific years he worked as a probation officer are not yet confirmed in the retrieved documents – only that he did work as one. Source Documents: • doc_9: “KEN WALIBORA: A Life, a Controversy in the US and an Unresolved…” | Confirmed probation officer role • doc_10: NTV Kenya article | Confirmed road accident death • doc_11: Hope Media article | Confirmed road accident death on Landhies Road, Nairobi • doc_18: “Kenyan author Walibora’s life and unresolved death” | Confirmed probation officer role • doc_19: “African Literature through the Lens of Ken Walibora: An Interview” (Aug 27, 2018) | Confirmed “no sell by date” quote 1) Facts & Evidence Collected • Author identified as Ken Walibora (Ken Walibora Waliaula), celebrated Kenyan Swahili writer and journalist. [Source: docs 9, 10, 11, 18, 19 | Verified: yes] • Died in a road accident: Run over by a vehicle on Landhies Road, Nairobi, April 2020. [Source: doc_10, doc_11 | Verified: yes] • Worked as a probation officer (and also as a teacher) before becoming a full-time writer and academic. [Source: doc_9, doc_18 | Verified: yes] • 2018 interview quote confirmed: “I often strive to write stories that have no sell by date, stories that I would find riveting myself, long after I have written them.” – from “African Literature through the Lens of Ken Walibora” interview, August 27, 2018. [Source: doc_19 | Verified: yes] • Lectured at a private university from 2018 until his death: Mentioned in clue context and consistent with his academic career; specific university not yet confirmed from search results. [Verified: partial] • Book selected as compulsory school reading in an African country in 2017: Not yet confirmed with specific title/country from search results. [Verified: not yet confirmed] • As a child, wanted to be a police officer: Not yet confirmed from search results. [Verified: not yet confirmed] • Specific years as probation officer: Not yet confirmed – need further search. [Verified: no] 2) Analysis & Conclusions • The author is definitively Ken Walibora: All clues align – road accident death in Nairobi, 2018 interview with the exact “no sell by date” quote, confirmed probation officer background, and Kenyan/African identity. • The question asks specifically which years Ken Walibora worked as a probation officer. Current sources confirm the role existed but do not specify the years. A more targeted search on his biography is needed. • Inference: Given his writing career and academic career trajectory, his probation officer work likely occurred in the 1990s or early 2000s, before he became a prominent writer – but this is unconfirmed. 3) Source Inventory & Verification Status Source What it contributed Verification Status doc_9: “KEN WALIBORA: A Life, a Controversy…” (Jan 2026) Confirmed probation officer role Partial – no years given doc_10: NTV Kenya article (Apr 2020) Confirmed road accident death Yes doc_11: Hope Media article (Apr 2020) Confirmed road accident, Landhies Road, Nairobi Yes doc_18: “Kenyan author Walibora’s life and unresolved death” (Jan 2026) Confirmed probation officer role Partial – no years given doc_19: “African Literature through the Lens of Ken Walibora” interview (Aug 2018) Confirmed “no sell by date” quote Yes 4) Uncertainties, Limitations, Gaps • Specific years Ken Walibora worked as a probation officer – the primary question – are not yet found. Need a more detailed biographical search. • Which private university he lectured at from 2018 until his death – not yet confirmed. • Which book was selected as compulsory school reading in 2017, and in which African country – not yet confirmed. • Childhood wish to be a police officer – not yet confirmed from search results. </summary> Round 2 (Planner) <think> Based on the summary, the author has been identified as Ken Walibora (Ken Walibora Waliaula), a Kenyan Swahili writer. The key remaining question is: which specific years did he work as a probation officer? The summary confirms he worked as a probation officer but doesn’t have the specific years. I need to search for more detailed biographical information about Ken Walibora’s career, specifically when he worked as a probation officer. Let me search for more specific information about his career timeline. </think> <tool_call> “name”: “search”, “arguments”: “query”: [“Ken Walibora probation officer years career biography”, “Ken Walibora Waliaula biography career timeline teacher probation”, “Ken Walibora ‘probation officer’ when years worked”] </tool_call> <tool_response> Query: “Ken Walibora probation officer years career biography” (3 documents) Document ID: doc_1 [webpage 1 begin] [webpage title] Ken Walibora – Senior Lecturer at Riara Universit – LinkedIn [webpage source query] Ken Walibora probation officer years career biography [webpage content begin] Probation officer, Ministry of Home affairs, Kenya 1988-96. High School English and Swahili teacher, Kenya 1985-86… Show more. EMPLOYMENT Assistant Professor … [webpage content end] [webpage 1 end] Document ID: doc_2 [webpage 2 begin] [webpage title] Ken Walibora: The Giant on Whose Shoulders I Stood – The Elephant … (As the content is too long, we will omit it here.) </tool_response> Round 2 (Synthesizer) <summary> 0) Latest Search Search Queries: 1. “Ken Walibora probation officer years career biography” – Intended to find the specific years he worked as a probation officer. Key result: doc_1 (LinkedIn) explicitly states “Probation officer, Ministry of Home affairs, Kenya 1988-96.” 2. “Ken Walibora Waliaula biography career timeline teacher probation” – Intended to confirm career timeline. Yielded Wikipedia entry, memorial articles, and CV references, but the most specific years came from doc_11 (Academia.edu CV). 3. “Ken Walibora ‘probation officer’ when years worked” – Intended to pinpoint the years. Key results: doc_11 (Academia.edu CV) and doc_16 (LinkedIn) both explicitly confirm “Probation officer, Ministry of Home Affairs, Kenya 1988-96.” Key Evidence Found: • Ken Walibora worked as a Probation Officer, Ministry of Home Affairs, Kenya from 1988 to 1996. This is confirmed by two independent sources: his LinkedIn profile and his Academia.edu CV. • He also worked as a High School English and Swahili teacher in Kenya 1985–86. • His first major novel Siku Njema was published in 1996, the same year his probation officer career ended – consistent with his transition to full-time writing. • He was a Senior Lecturer at Riara University (a private university in Kenya), confirmed by his LinkedIn profile. Source Documents: • doc_1 / doc_16: Ken Walibora LinkedIn profile | Confirmed probation officer 1988–96 and Riara University (private) lectureship • doc_11: Ken Walibora Curriculum Vitae (Academia.edu) | Confirmed probation officer 1988–96, teacher 1985–86 • doc_3 / doc_6: “Prof Ken Walibora buried in Cherang’any” | Confirmed teacher and probation officer background • doc_15: BBC article (Apr 26, 2020) | Confirmed Siku Njema published 1996, prolific writer with 40+ books 1) Facts & Evidence Collected • Author identified as Ken Walibora (full name: Kennedy Wafula Waliaula), pen name Ken Walibora. Kenyan writer, journalist, and scholar. Born January 6, 1965. [Source: Wikipedia (doc_4/doc_14) | Verified: yes] • Died in a road accident: Run over by a vehicle on Landhies Road, Nairobi, April 10, 2020. [Source: doc_17 (Hope Media), doc_10 (NTV Kenya) | Verified: yes] • Worked as a Probation Officer, Ministry of Home Affairs, Kenya from 1988 to 1996. [Source: doc_1/doc_16 (LinkedIn), doc_11 (Academia.edu CV) | Verified: yes – two independent primary sources] • Worked as a High School English and Swahili teacher in Kenya, 1985–86. [Source: doc_1/doc_16 (LinkedIn), doc_11 (Academia.edu CV) | Verified: yes] • Senior Lecturer at Riara University (a private university in Kenya) – consistent with “lectured at a private university from 2018 until his death.” [Source: doc_1/doc_16 (LinkedIn) | Verified: yes] • 2018 interview quote confirmed: “I often strive to write stories that have no sell by date, stories that I would find riveting myself, long after I have written them.” – from “African Literature through the Lens of Ken Walibora” interview, August 27, 2018. [Source: previously confirmed doc_19 (interview) | Verified: yes] • First major novel Siku Njema published in 1996, the same year his probation officer career ended. Published over 40 books total. [Source: doc_15 (BBC) | Verified: yes] • Book selected as compulsory school reading in an African country in 2017: Not yet confirmed with specific title/country. [Verified: not yet confirmed] • Childhood wish to be a police officer: Not yet confirmed from search results. [Verified: not yet confirmed] 2) Analysis & Conclusions • The author is definitively Ken Walibora: All clues fully align – road accident death in Nairobi (April 2020), 2018 “no sell by date” interview, probation officer background, lecturer at Riara University (private), and Kenyan/African identity. • The answer to the question is: Ken Walibora worked as a probation officer from 1988 to 1996 (Ministry of Home Affairs, Kenya). This is confirmed by two independent primary sources (his LinkedIn profile and his Academia.edu CV). • Career timeline coherence: Teacher (1985–86) → Probation Officer (1988–96) → Full-time writer/journalist (1996 onward, when Siku Njema was published) → Academic/Lecturer at Riara University (from 2018 until death in 2020). This trajectory is internally consistent and well-supported. • Riara University is a private university in Kenya, consistent with the clue about lecturing at a private university from 2018 until his death. 3) Source Inventory & Verification Status Source What it contributed Verification Status doc_1 / doc_16: Ken Walibora LinkedIn Profile Confirmed probation officer 1988–96; teacher 1985–86; Riara University lectureship Yes – primary source (his own professional profile) doc_11: Ken Walibora CV (Academia.edu) Confirmed probation officer 1988–96; teacher 1985–86 Yes – primary source (his own CV) doc_4 / doc_14: Wikipedia Full name, dates (1965–2020), Kenyan writer/journalist/scholar Yes doc_17: Hope Media (Apr 2020) Confirmed road accident death on Landhies Road, Nairobi Yes doc_15: BBC (Apr 26, 2020) Confirmed Siku Njema 1996, 40+ books Yes doc_3 / doc_6: “Prof Ken Walibora buried in Cherang’any” Confirmed teacher and probation officer background Yes (corroborating) Previously confirmed interview (Aug 2018) Confirmed “no sell by date” quote Yes 4) Uncertainties, Limitations, Gaps • Which specific book was selected as compulsory school reading in an African country in 2017 – not yet confirmed (likely a Kenyan school curriculum selection, possibly Siku Njema or another title, but unverified). • Childhood wish to be a police officer – not yet confirmed from search results (though the probation officer career in the Ministry of Home Affairs is adjacent to law enforcement, suggesting a possible connection). • All other key facts (author identity, years as probation officer, private university, 2018 interview quote, road accident death) are now confirmed. The primary question has been answered. </summary> Round 3 (Planner) <think> Based on the summary, the answer to the question is already well-established. Ken Walibora worked as a probation officer from 1988 to 1996, confirmed by two independent primary sources (his LinkedIn profile and his Academia.edu CV). Let me review all the clues in the question to make sure Ken Walibora is indeed the correct author: 1. “African author tragically passed away in a tragic road accident” ✓ – Ken Walibora died in a road accident on Landhies Road, Nairobi, April 10, 2020. 2. “As a child, he’d wanted to be a police officer” – Not yet confirmed but plausible given his probation officer career. 3. “He lectured at a private university from 2018 until his death” ✓ – Senior Lecturer at Riara University (private university in Kenya) from 2018. 4. “In 2018, this author spoke about writing stories that have no sell by date in an interview” ✓ – Confirmed from August 27, 2018 interview. 5. “One of his books was selected to be a compulsory school reading in an African country in 2017” – Not yet confirmed specifically. 6. “Which years did this author work as a probation officer?” – Answer: 1988 to 1996. The summary has confirmed the primary question answer. The author is Ken Walibora, and he worked as a probation officer from 1988 to 1996. The question asks specifically “Which years did this author work as a probation officer?” – The answer is 1988 to 1996 (or 1988–1996). I have sufficient information to answer this question confidently. The evidence is from two independent primary sources (his LinkedIn profile and his Academia.edu CV), both stating “Probation officer, Ministry of Home Affairs, Kenya 1988-96.” I can now provide the final answer. </think> <answer> The African author described in the question is Ken Walibora (Kennedy Wafula Waliaula), a prolific Kenyan writer, journalist, and scholar who tragically died after being struck by a vehicle on Landhies Road, Nairobi, on April 10, 2020. Answer Ken Walibora worked as a probation officer from 1988 to 1996 (Ministry of Home Affairs, Kenya). Supporting Evidence and Career Context This is confirmed by two independent primary sources – his LinkedIn professional profile and his Curriculum Vitae (hosted on Academia.edu) – both of which explicitly list: “Probation Officer, Ministry of Home Affairs, Kenya, 1988–1996.” His career timeline is coherent and well-documented: Period Role 1985–1986 High School English and Swahili Teacher, Kenya 1988–1996 Probation Officer, Ministry of Home Affairs, Kenya 1996 onward Full-time writer/journalist (debut novel Siku Njema published 1996) 2018–2020 Senior Lecturer, Riara University (a private university in Nairobi) Why Ken Walibora Matches All the Clues • Tragic road accident death: He was fatally struck by a vehicle on Landhies Road, Nairobi, on April 10, 2020. • Childhood wish to be a police officer: His early aspiration toward law enforcement is consistent with his later career as a probation officer under the Ministry of Home Affairs – a role closely adjacent to policing and criminal justice. • Lectured at a private university from 2018 until his death: He was a Senior Lecturer at Riara University, a private institution in Kenya, from 2018 until his passing in 2020. • 2018 interview about “no sell by date”: In an August 27, 2018 interview titled “African Literature through the Lens of Ken Walibora,” he stated: “I often strive to write stories that have no sell by date, stories that I would find riveting myself, long after I have written them.” • Book selected as compulsory school reading in 2017: His works, including his celebrated debut Siku Njema (1996), were incorporated into African school curricula. • African author: Ken Walibora was Kenyan, born January 6, 1965, and wrote primarily in Swahili, becoming one of East Africa’s most celebrated literary figures with over 40 published books. In summary, Ken Walibora served as a probation officer from 1988 to 1996. </answer>