title: WebThinker: Empowering Large Reasoning Models with Deep Research
    Capability
summary: Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate
  impressive long-horizon reasoning capabilities. However, their reliance on
  static internal knowledge limits their performance on complex,
  knowledge-intensive tasks and hinders their ability to produce comprehensive
  research reports requiring synthesis of diverse web information. To address
  this, we propose WebThinker, a deep research agent that empowers LRMs
  to autonomously search the web, navigate web pages, and draft research reports
  during the reasoning process. WebThinker integrates a Deep Web
  Explorer module, enabling LRMs to dynamically search, navigate, and extract
  information from the web when encountering knowledge gaps. It also employs an
  Autonomous Think-Search-and-Draft strategy, allowing the model to
  seamlessly interleave reasoning, information gathering, and report writing in
  real time. To further enhance research tool utilization, we introduce an
  RL-based training strategy via iterative online Direct Preference
  Optimization (DPO). Extensive experiments on complex reasoning benchmarks
  (GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive)
  demonstrate that WebThinker significantly outperforms existing methods and
  strong proprietary systems. Our approach enhances LRM reliability and
  applicability in complex scenarios, paving the way for more capable and
  versatile deep research systems. The code is available at
  https://github.com/RUC-NLPIR/WebThinker.
paper_authors._id: 6812d593060494e99e4835e0
paper_authors.name: Xiaoxi Li
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paper_authors.fullname: Xiaoxi Li
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paper_authors._id: 6812d593060494e99e4835e1
paper_authors.name: Jiajie Jin
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paper_authors.fullname: Jiajie Jin
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paper_authors.user: jinjiajie
paper_authors._id: 6812d593060494e99e4835e2
paper_authors.name: Guanting Dong
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paper_authors.fullname: KABI
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paper_authors.user: dongguanting
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paper_authors.name: Hongjin Qian
paper_authors._id: 6812d593060494e99e4835e4
paper_authors.name: Yutao Zhu
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paper_authors.fullname: Yutao Zhu
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paper_authors.user: yutaozhu94
paper_authors._id: 6812d593060494e99e4835e5
paper_authors.name: Yongkang Wu
paper_authors.status: admin_assigned
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paper_authors.fullname: YongkangWu
paper_authors.type: user
paper_authors.user: wuyongkang
paper_authors._id: 6812d593060494e99e4835e6
paper_authors.name: Ji-Rong Wen
paper_authors.status: admin_assigned
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paper_authors._id: 64b8c89052b7353d8c6a1013
paper_authors.avatarUrl: /avatars/cd59fffe81f6b07b4519540b8ff3d95f.svg
paper_authors.fullname: Ji-Rong Wen
paper_authors.type: user
paper_authors.user: jrwen
paper_authors._id: 6812d593060494e99e4835e7
paper_authors.name: Zhicheng Dou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-05T07:52:39.068Z
paper_authors._id: 66f0bf59e9d50ec57febf751
paper_authors.avatarUrl: /avatars/be97941e60064e5dd806c6fe9db3c537.svg
paper_authors.fullname: Zhicheng Dou
paper_authors.type: user
paper_authors.user: douzc
paper_title: WebThinker: Empowering Large Reasoning Models with Deep Research
    Capability
paper_summary: Large reasoning models (LRMs), such as OpenAI-o1 and DeepSeek-R1, demonstrate
  impressive long-horizon reasoning capabilities. However, their reliance on
  static internal knowledge limits their performance on complex,
  knowledge-intensive tasks and hinders their ability to produce comprehensive
  research reports requiring synthesis of diverse web information. To address
  this, we propose WebThinker, a deep research agent that empowers LRMs
  to autonomously search the web, navigate web pages, and draft research reports
  during the reasoning process. WebThinker integrates a Deep Web
  Explorer module, enabling LRMs to dynamically search, navigate, and extract
  information from the web when encountering knowledge gaps. It also employs an
  Autonomous Think-Search-and-Draft strategy, allowing the model to
  seamlessly interleave reasoning, information gathering, and report writing in
  real time. To further enhance research tool utilization, we introduce an
  RL-based training strategy via iterative online Direct Preference
  Optimization (DPO). Extensive experiments on complex reasoning benchmarks
  (GPQA, GAIA, WebWalkerQA, HLE) and scientific report generation tasks (Glaive)
  demonstrate that WebThinker significantly outperforms existing methods and
  strong proprietary systems. Our approach enhances LRM reliability and
  applicability in complex scenarios, paving the way for more capable and
  versatile deep research systems. The code is available at
  https://github.com/RUC-NLPIR/WebThinker.
paper_ai_summary: WebThinker, an autonomous deep research agent, improves LRM performance on complex tasks by integrating a Deep Web Explorer for dynamic web information gathering and using RL-based training through Direct Preference Optimization.
paper_ai_keywords: Large reasoning models (LRMs) WebThinker Deep Web Explorer Autonomous Think-Search-and-Draft RL-based training Direct Preference Optimization (DPO) GPQA GAIA WebWalkerQA HLE Glaive