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

• URL: http://arxiv.org/abs/2505.11277v1
• Date: Fri, 16 May 2025 14:11:29 GMT
• Title: Search and Refine During Think: Autonomous Retrieval-Augmented Reasoning of LLMs
• Authors: Yaorui Shi, Shihan 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: 23.281040584710635
• 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.

Related papers

• Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs [69.10441885629787]
  Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge.<n>It falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts.<n>This survey synthesizes both strands under a unified reasoning-retrieval perspective.
  arXiv  Detail & Related papers  (2025-07-13T03:29:41Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• SEM: Reinforcement Learning for Search-Efficient Large Language Models [26.075903427834838]
  Large Language Models (LLMs) have demonstrated their capabilities not only in reasoning but also in invoking external tools.<n>Existing reinforcement learning approaches often lead to redundant search behaviors, resulting in inefficiencies and over-cost.<n>We propose SEM, a novel post-training reinforcement learning framework that explicitly trains LLMs to optimize search usage.
  arXiv  Detail & Related papers  (2025-05-12T09:45:40Z)
• R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning [87.30285670315334]
  textbfR1-Searcher is a novel two-stage outcome-based RL approach designed to enhance the search capabilities of Large Language Models.<n>Our framework relies exclusively on RL, without requiring process rewards or distillation for a cold start.<n>Our experiments demonstrate that our method significantly outperforms previous strong RAG methods, even when compared to the closed-source GPT-4o-mini.
  arXiv  Detail & Related papers  (2025-03-07T17:14:44Z)
• DeepRAG: Thinking to Retrieve Step by Step for Large Language Models [92.87532210660456]
  We propose DeepRAG, a framework that models retrieval-augmented reasoning as a Markov Decision Process (MDP)<n>By iteratively decomposing queries, DeepRAG dynamically determines whether to retrieve external knowledge or rely on parametric reasoning at each step.<n> Experiments show that DeepRAG improves retrieval efficiency and boosts answer accuracy by 26.4%, demonstrating its effectiveness in enhancing retrieval-augmented reasoning.
  arXiv  Detail & Related papers  (2025-02-03T08:22:45Z)
• Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability [19.722009684115434]
  Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks.<n>Existing RAG frameworks, which usually follows the retrieve-then-read paradigm, often struggle with multi-hop QA with temporal information.<n>This paper proposes a novel framework called review-then-refine, which aims to enhance LLM performance in multi-hop QA scenarios with temporal information.
  arXiv  Detail & Related papers  (2024-12-19T17:48:23Z)
• RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement [85.08223786819532]
  Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks.<n>We propose textbfRAG-Star, a novel RAG approach that integrates retrieved information to guide the tree-based deliberative reasoning process.<n>Our experiments involving Llama-3.1-8B-Instruct and GPT-4o demonstrate that RAG-Star significantly outperforms previous RAG and reasoning methods.
  arXiv  Detail & Related papers  (2024-12-17T13:05:36Z)
• Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models [31.769428095250912]
  Auto-RAG is an autonomous iterative retrieval model centered on the reasoning capabilities of Large Language Models (LLMs)<n>We develop a method for autonomously synthesizing reasoning-based decision-making instructions in iterative retrieval.<n>Auto-RAG expresses the iterative retrieval process in natural language, enhancing interpretability.
  arXiv  Detail & Related papers  (2024-11-29T03:01:05Z)
• Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models [84.15513004135576]
  Current research enhances the reasoning performance of Large Language Models (LLMs) by sampling multiple reasoning chains and ensembling based on the answer frequency. This approach fails in scenarios where the correct answers are in the minority. We introduce a hierarchical reasoning aggregation framework AoR, which selects answers based on the evaluation of reasoning chains.
  arXiv  Detail & Related papers  (2024-05-21T17:12:19Z)
• HGOT: Hierarchical Graph of Thoughts for Retrieval-Augmented In-Context Learning in Factuality Evaluation [20.178644251662316]
  We introduce the hierarchical graph of thoughts (HGOT) to enhance the retrieval of pertinent passages during in-context learning. The framework employs the divide-and-conquer strategy to break down complex queries into manageable sub-queries. It refines self-consistency majority voting for answer selection, which incorporates the recently proposed citation recall and precision metrics.
  arXiv  Detail & Related papers  (2024-02-14T18:41:19Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.