{"ID":6267723,"CreatedAt":"2026-07-10T01:11:38.759438437Z","UpdatedAt":"2026-07-11T17:47:58.155493336Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07820","arxiv_id":"2607.07820","title":"DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.07820","url_pdf":"https://arxiv.org/pdf/2607.07820v1","authors":"[\"Xinyu Geng\",\"Xuanhua He\",\"Sixiang Chen\",\"Yanjing Xiao\",\"Fan Zhang\",\"Shijue Huang\",\"Haitao Mi\",\"Zhenwen Liang\",\"Tianqing Fang\",\"Yi R. Fung\"]","published":"2026-07-08T18:03:41Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}