ReaRAG：知识引导的推理通过迭代检索增强生成提升大推理模型的事实性
ReaRAG: Knowledge-guided Reasoning Enhances Factuality of Large Reasoning Models with Iterative Retrieval Augmented Generation
AI总结：
针对大推理模型事实准确性不足、带检索能力的RL类模型存在过度思考且推理鲁棒性差的问题，提出带推理链长度上限的ReaRAG模型，通过迭代检索增强生成提升事实性，在多跳问答上优于基线，兼具强反思能力。
摘要：
大推理模型（LRMs）展现出卓越的推理能力，但主要依赖参数化知识，限制了事实准确性。尽管近期研究为基于强化学习（RL）的大推理模型配备了检索能力，但这类模型存在过度思考问题，且推理缺乏鲁棒性，降低了其在问答（QA）任务中的效用。为解决该问题，我们提出ReaRAG——一种事实性增强的推理模型，可在不产生过多迭代的前提下探索多样化查询。我们的方案包含一个带推理链长度上限的新型数据构建框架。具体而言，我们首先利用大推理模型生成审慎思考过程，再从预定义动作空间（搜索Search和结束Finish）中选择动作。对于搜索动作，会向RAG引擎执行查询，返回的结果作为观测值以指导后续推理步骤，该过程迭代进行直至选择结束动作。得益于ReaRAG强大的推理能力，我们的方法在多跳问答任务上优于现有基线模型。进一步分析凸显了其识别错误、优化推理轨迹的强反思能力。本研究在提升大推理模型事实性的同时，有效为检索增强生成（RAG）整合了鲁棒推理能力。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
