Retrieval-Augmented Generation (RAG) augments Large Language Models (LLMs) with external knowledge to improve factuality. However, existing RAG systems frequently underutilize the retrieved documents, failing to extract and integrate the key clues needed to support faithful and interpretable reasoning, especially in cases where relevant evidence is implicit, scattered, or obscured by noise. To address this issue, we propose ClueAnchor, a novel framework for enhancing RAG via clue-anchored reasoning exploration and optimization. ClueAnchor extracts key clues from retrieved content and generates multiple reasoning paths based on different knowledge configurations, optimizing the model by selecting the most appropriate reasoning path for the given context through reward-based preference optimization. Experiments show that ClueAnchor significantly outperforms prior RAG baselines in the completeness and robustness of reasoning. Further analysis confirms its strong resilience to noisy or partially relevant retrieved content, as well as its capability to identify supporting evidence even in the absence of explicit clue supervision during inference. All codes are available at https://github.com/thunlp/ClueAnchor.

翻译：检索增强生成（RAG）通过引入外部知识来增强大语言模型（LLMs）的事实准确性。然而，现有RAG系统常常未能充分利用检索到的文档，未能提取和整合支持忠实且可解释推理所需的关键线索，尤其是在相关证据隐含、分散或被噪声掩盖的情况下。为解决这一问题，我们提出了ClueAnchor，一种通过线索锚定推理探索与优化来增强RAG的新型框架。ClueAnchor从检索内容中提取关键线索，并基于不同知识配置生成多条推理路径，通过基于奖励的偏好优化为给定上下文选择最合适的推理路径来优化模型。实验表明，ClueAnchor在推理的完整性和鲁棒性方面显著优于先前的RAG基线方法。进一步分析证实了其对噪声或部分相关检索内容的强大适应能力，以及在推理过程中无需显式线索监督的情况下识别支持证据的能力。所有代码已在https://github.com/thunlp/ClueAnchor开源。