title: Search-o1: Agentic Search-Enhanced Large Reasoning Models
summary: Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive
  long stepwise reasoning capabilities through large-scale reinforcement
  learning. However, their extended reasoning processes often suffer from
  knowledge insufficiency, leading to frequent uncertainties and potential
  errors. To address this limitation, we introduce Search-o1, a
  framework that enhances LRMs with an agentic retrieval-augmented generation
  (RAG) mechanism and a Reason-in-Documents module for refining retrieved
  documents. Search-o1 integrates an agentic search workflow into the reasoning
  process, enabling dynamic retrieval of external knowledge when LRMs encounter
  uncertain knowledge points. Additionally, due to the verbose nature of
  retrieved documents, we design a separate Reason-in-Documents module to deeply
  analyze the retrieved information before injecting it into the reasoning chain,
  minimizing noise and preserving coherent reasoning flow. Extensive experiments
  on complex reasoning tasks in science, mathematics, and coding, as well as six
  open-domain QA benchmarks, demonstrate the strong performance of Search-o1.
  This approach enhances the trustworthiness and applicability of LRMs in complex
  reasoning tasks, paving the way for more reliable and versatile intelligent
  systems. The code is available at
  https://github.com/sunnynexus/Search-o1.
paper_authors._id: 678084b5883142429f3cf7a0
paper_authors.name: Xiaoxi Li
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paper_authors.name: Guanting Dong
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paper_authors.name: Jiajie Jin
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paper_authors.fullname: Jiajie Jin
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paper_authors.name: Yuyao Zhang
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paper_authors.fullname: Yuyao Zhang
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paper_authors.name: Yujia Zhou
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paper_authors.name: Yutao Zhu
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paper_authors.fullname: Yutao Zhu
paper_authors.type: user
paper_authors.user: yutaozhu94
paper_authors._id: 678084b5883142429f3cf7a6
paper_authors.name: Peitian Zhang
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paper_authors.fullname: NaN
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paper_authors._id: 678084b5883142429f3cf7a7
paper_authors.name: Zhicheng Dou
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paper_authors.fullname: Zhicheng Dou
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paper_title: Search-o1: Agentic Search-Enhanced Large Reasoning Models
paper_summary: Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive
  long stepwise reasoning capabilities through large-scale reinforcement
  learning. However, their extended reasoning processes often suffer from
  knowledge insufficiency, leading to frequent uncertainties and potential
  errors. To address this limitation, we introduce Search-o1, a
  framework that enhances LRMs with an agentic retrieval-augmented generation
  (RAG) mechanism and a Reason-in-Documents module for refining retrieved
  documents. Search-o1 integrates an agentic search workflow into the reasoning
  process, enabling dynamic retrieval of external knowledge when LRMs encounter
  uncertain knowledge points. Additionally, due to the verbose nature of
  retrieved documents, we design a separate Reason-in-Documents module to deeply
  analyze the retrieved information before injecting it into the reasoning chain,
  minimizing noise and preserving coherent reasoning flow. Extensive experiments
  on complex reasoning tasks in science, mathematics, and coding, as well as six
  open-domain QA benchmarks, demonstrate the strong performance of Search-o1.
  This approach enhances the trustworthiness and applicability of LRMs in complex
  reasoning tasks, paving the way for more reliable and versatile intelligent
  systems. The code is available at
  https://github.com/sunnynexus/Search-o1.
paper_ai_summary: Search-o1 enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module to improve performance on complex reasoning tasks.
paper_ai_keywords: Large reasoning models reinforcement learning knowledge insufficiency Search-o1 agentic retrieval-augmented generation Reason-in-Documents module external knowledge complex reasoning tasks science mathematics coding open-domain QA benchmarks trustworthiness reliability