IterSynth: Rethinking Deep Search Agents via Role-Decoupled Iterative Synthesis
IterSynth decouples planning and synthesis for deep-search agents, and an 8B model scores 50.7.
IterSynth splits deep search into a Planner that identifies information needs and a Synthesizer that folds evidence into an evolving summary, avoiding ReAct-style role coupling and noisy accumulated context. Training uses Role-Decoupled Policy Optimization, combining terminal outcome rewards with turn-level rubric scores and role-specific advantages. On five long-horizon benchmarks including BrowseComp and Xbench-DS, IterSynth-8B averages 50.7, surpassing the strongest prior agent at or below 8B by 4.2%. The same design also works as a model-agnostic prompting paradigm with zero-shot gains on frontier proprietary models.
- A Planner finds information needs; a Synthesizer updates a persistent summary.
- RDPO mixes outcome rewards, turn-level rubrics, and role-specific advantages.
- IterSynth-8B averages 50.7, ahead of the best prior ≤8B agent by 4.2%.
- As a prompting pattern it also improves frontier models over ReAct zero-shot.
From the original article
1 min read at huggingface.co
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.
By Xingyu Wu, Yuchen Yan, Zhengxi Lu · huggingface.co
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