[Submitted on 8 Jul 2026]
Title:DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment
View a PDF of the paper titled DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment, by Xinyu Geng and 9 other authors
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Abstract:Training tool-use agents to improve from their own experience remains challenging, as supervised fine-tuning relies on fixed teacher-distilled trajectories, while sparse-reward reinforcement learning provides weak supervision for long-horizon interactions. We present DeepSearch-Evolve, a self-distillation framework for web agents built on DeepSearch-World, a deterministic and verifiable environment with reproducible search and page-reading tools. DeepSearch-World contains 420K multi-hop QA tasks constructed from entity-level random walks and supports key agentic cognitive behaviors useful for self-evolving, including progress verification, grounded reflection, and failure recovery. DeepSearch-Evolve iteratively performs trajectory generation, filtering, data mixing, and fine-tuning to train stronger agents. Without distillation from more capable models, DeepSearch-World-9B achieves competitive performance compared with open-source agents, reaching 31.2% on BrowseComp, 61.5% on GAIA, and 93.4% on HotpotQA, showing that verifiable environments enable scalable self-evolution for long-horizon web agents. We will release the environment, 420K training pool, validation set, model, and code to facilitate future research on self-improving deep search agents.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.07820 [cs.CL]
(or arXiv:2607.07820v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.07820
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Xinyu Geng [view email] [v1] Wed, 8 Jul 2026 18:03:41 UTC (2,480 KB)
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