WebThinker: Empowering Large Reasoning Models with Deep Research
  Capability
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.
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Xiaoxi Li
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2025-05-02T06:35:05.719Z
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Xiaoxi Li
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Jiajie Jin
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2025-05-05T07:51:30.352Z
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Jiajie Jin
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jinjiajie
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Guanting Dong
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2025-05-05T07:51:43.904Z
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KABI
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dongguanting
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Hongjin Qian
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Yutao Zhu
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2025-05-05T07:52:05.326Z
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Yutao Zhu
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Yongkang Wu
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2025-05-05T07:52:24.526Z
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YongkangWu
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Ji-Rong Wen
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2025-05-05T07:52:32.940Z
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Ji-Rong Wen
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Zhicheng Dou
admin_assigned
2025-05-05T07:52:39.068Z
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Zhicheng Dou
user
douzc
WebThinker: Empowering Large Reasoning Models with Deep Research
  Capability
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.
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.
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