ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large
  Reasoning Models with Iterative Retrieval Augmented Generation
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).
67e610ee6e73232cf0903b73
Zhicheng Lee
claimed_verified
2025-03-28T08:37:06.279Z
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Lee Zhicheng
user
ZhiCheng0326
67e610ee6e73232cf0903b74
Shulin Cao
admin_assigned
2025-03-28T08:52:53.008Z
62c924a6334a6ee11c2e8dfa
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ShulinCao
user
caoshulin
67e610ee6e73232cf0903b75
Jinxin Liu
67e610ee6e73232cf0903b76
Jiajie Zhang
admin_assigned
2025-03-28T08:53:38.290Z
66cdd285c51a915bd5f2d017
/avatars/14e5794307e4672b1b51d26b31227e0f.svg
Jiajie Zhang
user
NeoZ123
67e610ee6e73232cf0903b77
Weichuan Liu
admin_assigned
2025-03-28T08:53:49.672Z
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/avatars/faf9d96770251f31e7e4edbf1fee9798.svg
liuweichuan
user
liuweichuan
67e610ee6e73232cf0903b78
Xiaoyin Che
admin_assigned
2025-03-28T08:53:55.317Z
65d66cb2b06abf924b07ff76
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Xiaoyin Chen
user
chenyn66
67e610ee6e73232cf0903b79
Lei Hou
67e610ee6e73232cf0903b7a
Juanzi Li
admin_assigned
2025-03-28T08:54:01.074Z
65df8cbc2705d9672f55d1aa
/avatars/63e46f15bb76bd9d4508fd0f54f39829.svg
Juanzi Li
user
juanli
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large
  Reasoning Models with Iterative Retrieval Augmented Generation
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).
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.
ReaRAG factuality-enhanced reasoning model reasoning chain length action space Search Finish RAG engine reflective ability Retrieval-Augmented Generation