title: ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large
    Reasoning Models with Iterative Retrieval Augmented Generation
summary: Large Reasoning Models (LRMs) exhibit remarkable reasoning abilities but rely
  primarily on parametric knowledge, limiting factual accuracy. While recent
  works equip reinforcement learning (RL)-based LRMs with retrieval capabilities,
  they suffer from overthinking and lack robustness in reasoning, reducing their
  effectiveness in question answering (QA) tasks. To address this, we propose
  ReaRAG, a factuality-enhanced reasoning model that explores diverse queries
  without excessive iterations. Our solution includes a novel data construction
  framework with an upper bound on the reasoning chain length. Specifically, we
  first leverage an LRM to generate deliberate thinking, then select an action
  from a predefined action space (Search and Finish). For Search action, a query
  is executed against the RAG engine, where the result is returned as observation
  to guide reasoning steps later. This process iterates until a Finish action is
  chosen. Benefiting from ReaRAG's strong reasoning capabilities, our approach
  outperforms existing baselines on multi-hop QA. Further analysis highlights its
  strong reflective ability to recognize errors and refine its reasoning
  trajectory. Our study enhances LRMs' factuality while effectively integrating
  robust reasoning for Retrieval-Augmented Generation (RAG).
paper_authors._id: 67e610ee6e73232cf0903b73
paper_authors.name: Zhicheng Lee
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paper_authors.fullname: Lee Zhicheng
paper_authors.type: user
paper_authors.user: ZhiCheng0326
paper_authors._id: 67e610ee6e73232cf0903b74
paper_authors.name: Shulin Cao
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paper_authors.fullname: ShulinCao
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paper_authors.user: caoshulin
paper_authors._id: 67e610ee6e73232cf0903b75
paper_authors.name: Jinxin Liu
paper_authors._id: 67e610ee6e73232cf0903b76
paper_authors.name: Jiajie Zhang
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paper_authors.fullname: Jiajie Zhang
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paper_authors.user: NeoZ123
paper_authors._id: 67e610ee6e73232cf0903b77
paper_authors.name: Weichuan Liu
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paper_authors.fullname: liuweichuan
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paper_authors.user: liuweichuan
paper_authors._id: 67e610ee6e73232cf0903b78
paper_authors.name: Xiaoyin Che
paper_authors.status: admin_assigned
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paper_authors._id: 65d66cb2b06abf924b07ff76
paper_authors.avatarUrl: /avatars/de94e2fe07040b7dc3053bcaafa64ffe.svg
paper_authors.fullname: Xiaoyin Chen
paper_authors.type: user
paper_authors.user: chenyn66
paper_authors._id: 67e610ee6e73232cf0903b79
paper_authors.name: Lei Hou
paper_authors._id: 67e610ee6e73232cf0903b7a
paper_authors.name: Juanzi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-28T08:54:01.074Z
paper_authors._id: 65df8cbc2705d9672f55d1aa
paper_authors.avatarUrl: /avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
paper_authors.fullname: Juanzi Li
paper_authors.type: user
paper_authors.user: juanli
paper_title: ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large
    Reasoning Models with Iterative Retrieval Augmented Generation
paper_summary: Large Reasoning Models (LRMs) exhibit remarkable reasoning abilities but rely
  primarily on parametric knowledge, limiting factual accuracy. While recent
  works equip reinforcement learning (RL)-based LRMs with retrieval capabilities,
  they suffer from overthinking and lack robustness in reasoning, reducing their
  effectiveness in question answering (QA) tasks. To address this, we propose
  ReaRAG, a factuality-enhanced reasoning model that explores diverse queries
  without excessive iterations. Our solution includes a novel data construction
  framework with an upper bound on the reasoning chain length. Specifically, we
  first leverage an LRM to generate deliberate thinking, then select an action
  from a predefined action space (Search and Finish). For Search action, a query
  is executed against the RAG engine, where the result is returned as observation
  to guide reasoning steps later. This process iterates until a Finish action is
  chosen. Benefiting from ReaRAG's strong reasoning capabilities, our approach
  outperforms existing baselines on multi-hop QA. Further analysis highlights its
  strong reflective ability to recognize errors and refine its reasoning
  trajectory. Our study enhances LRMs' factuality while effectively integrating
  robust reasoning for Retrieval-Augmented Generation (RAG).
paper_ai_summary: ReaRAG enhances large reasoning models by integrating retrieval capabilities with a novel data construction framework, improving factuality and robust reasoning for multi-hop question answering tasks.
paper_ai_keywords: ReaRAG factuality-enhanced reasoning model reasoning chain length action space Search Finish RAG engine reflective ability Retrieval-Augmented Generation