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WebThinker: Empowering Large Reasoning Models with Deep Research Capability

Paper · 2025 · Xiaoxi Li, Jiajie Jin, Guanting Dong et al.

Summary

Large reasoning models such as DeepSeek-R1 and QwQ handle long reasoning chains well, but they can only use what they memorized during training. This weakens them on knowledge-heavy questions and on research reports that must combine many web sources. WebThinker turns such a model into an open-source deep research agent.

While reasoning, the model can call a Deep Web Explorer. This module runs searches, follows links or buttons on pages, and returns the facts it extracts. In report mode, the Autonomous Think-Search-and-Draft strategy adds three tools: one writes a section, one inspects the current draft, and one revises it. Writing therefore happens alongside searching rather than only at the end. The authors further tune tool use with iterative online DPO, using preference pairs built from sampled trajectories.

Built on QwQ-32B, the system beats Search-o1 and RAG baselines on GPQA, GAIA, WebWalkerQA and HLE. On Glaive report generation it also scores above Grok3 DeeperSearch and Gemini2.0 Deep Research. The work suggests open models can rival commercial deep research products.

Key points

  • WebThinker-32B-RL (QwQ-32B backbone) reaches 70.7 on GPQA, 48.5 on GAIA, 46.5 on WebWalkerQA and 15.8 on HLE. The authors report relative gains over Search-o1 of 21.9% on GAIA and 36.2% on HLE, and say it beats o3-mini (High) on HLE.
  • On Glaive scientific reports, judged on completeness, thoroughness, factuality and coherence, it gets an overall score of 8.0 versus 7.9 for Gemini Deep Research.
  • In ablations, removing the Deep Web Explorer lowers GAIA from 48.5 to 38.8. Removing autonomous drafting drops the report average to 6.6.
  • The framework transfers to DeepSeek-R1 backbones of 7B, 14B and 32B, using cold-start SFT on 7.8k trajectories followed by RL. The 7B variant gains 174.4% on GAIA over direct generation.
  • The paper was published at NeurIPS 2025, with code at github.com/RUC-NLPIR/WebThinker. Its stated limitations are no multimodal input, a small toolset and no GUI-based browsing.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Renmin University of ChinaBeijing · China139 works · 17,752 citations🔬Beijing Academy of Artificial IntelligenceBeijing · China46 works · 8,682 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 worksInformation Systems · 1,730 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,685 works

Cite

Cite as Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yongkang Wu, Ji-Rong Wen, Yutao Zhu and Zhicheng Dou. 2025. WebThinker: Empowering Large Reasoning Models with Deep Research Capability. arXiv:2504.21776.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2504.21776 · arXiv 2504.21776

@misc{li2025webthinker,
  title = {{WebThinker: Empowering Large Reasoning Models with Deep Research Capability}},
  author = {Xiaoxi Li, Jiajie Jin, Guanting Dong, Hongjin Qian, Yongkang Wu, Ji-Rong Wen, Yutao Zhu and Zhicheng Dou},
  year = {2025},
  doi = {10.48550/arxiv.2504.21776},
  eprint = {2504.21776},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2504.21776}
}

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