Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs

• URL: http://arxiv.org/abs/2505.11277v2
• Date: Wed, 28 May 2025 02:19:51 GMT
• Title: Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs
• Authors: Yaorui Shi, Sihang Li, Chang Wu, Zhiyuan Liu, Junfeng Fang, Hengxing Cai, An Zhang, Xiang Wang,
• Abstract summary: Large language models have demonstrated impressive reasoning capabilities but are inherently limited by their knowledge reservoir.<n>Retrieval-augmented reasoning mitigates this limitation by allowing LLMs to query external resources.<n>We propose AutoRefine, a reinforcement learning framework that adopts a new search-and-refine-during-think'' paradigm.
• Score: 25.800565994304847
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large language models have demonstrated impressive reasoning capabilities but are inherently limited by their knowledge reservoir. Retrieval-augmented reasoning mitigates this limitation by allowing LLMs to query external resources, but existing methods often retrieve irrelevant or noisy information, hindering accurate reasoning. In this paper, we propose AutoRefine, a reinforcement learning post-training framework that adopts a new ``search-and-refine-during-think'' paradigm. AutoRefine introduces explicit knowledge refinement steps between successive search calls, enabling the model to iteratively filter, distill, and organize evidence before generating an answer. Furthermore, we incorporate tailored retrieval-specific rewards alongside answer correctness rewards using group relative policy optimization. Experiments on single-hop and multi-hop QA benchmarks demonstrate that AutoRefine significantly outperforms existing approaches, particularly in complex, multi-hop reasoning scenarios. Detailed analysis shows that AutoRefine issues frequent, higher-quality searches and synthesizes evidence effectively.

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