| title | DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment |
|---|---|
| authors | Xinyu Geng, Xuanhua He, Sixiang Chen, Yanjing Xiao, Fan Zhang, Shijue Huang, Haitao Mi, Zhenwen Liang, Tianqing Fang, Yi R. Fung |
| date | 2026-07-08 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.07820 |
| affiliation | HKUST |
| upvotes | 94 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| 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. |
cite
@misc{geng2026deepsearch,
title={DeepSearch-World: Self-Distillation for Deep Search Agents in a Verifiable Environment},
author={Xinyu Geng and Xuanhua He and Sixiang Chen and Yanjing Xiao and Fan Zhang and Shijue Huang and Haitao Mi and Zhenwen Liang and Tianqing Fang and Yi R. Fung},
year={2026},
eprint={2607.07820},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.07820}
}