Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for Large Language Models (LLMs) to address knowledge-intensive queries requiring domain-specific or up-to-date information. To handle complex multi-hop questions that are challenging for single-step retrieval, iterative RAG approaches incorporating reinforcement learning have been proposed. However, existing iterative RAG systems typically plan to decompose questions without leveraging information about the available retrieval corpus, leading to inefficient retrieval and reasoning chains that cascade into suboptimal performance. In this paper, we introduce Early Knowledge Alignment (EKA), a simple but effective module that aligns LLMs with retrieval set before planning in iterative RAG systems with contextually relevant retrieved knowledge. Extensive experiments on six standard RAG datasets demonstrate that by establishing a stronger reasoning foundation, EKA significantly improves retrieval precision, reduces cascading errors, and enhances both performance and efficiency. Our analysis from an entropy perspective demonstrate that incorporating early knowledge reduces unnecessary exploration during the reasoning process, enabling the model to focus more effectively on relevant information subsets. Moreover, EKA proves effective as a versatile, training-free inference strategy that scales seamlessly to large models. Generalization tests across diverse datasets and retrieval corpora confirm the robustness of our approach. Overall, EKA advances the state-of-the-art in iterative RAG systems while illuminating the critical interplay between structured reasoning and efficient exploration in reinforcement learning-augmented frameworks. The code is released at \href{https://github.com/yxzwang/EarlyKnowledgeAlignment}{Github}.

翻译：检索增强生成（RAG）已成为大型语言模型（LLMs）处理需要领域特定或最新信息的知识密集型查询的强大范式。为应对单步检索难以处理的复杂多跳问题，研究者提出了结合强化学习的迭代式RAG方法。然而，现有迭代式RAG系统通常在规划问题分解时未充分利用可用检索语料库的信息，导致检索效率低下，推理链级联误差累积，最终影响性能。本文提出早期知识对齐（EKA），这是一个简单而有效的模块，在迭代式RAG系统中进行规划前，通过上下文相关的检索知识使LLMs与检索集对齐。在六个标准RAG数据集上的大量实验表明，通过建立更坚实的推理基础，EKA显著提高了检索精度，减少了级联错误，并同时提升了性能与效率。我们从熵的角度进行分析，证明引入早期知识能减少推理过程中不必要的探索，使模型更有效地聚焦于相关信息子集。此外，EKA被证明是一种无需训练、通用性强的推理策略，可无缝扩展至大型模型。跨多样化数据集和检索语料库的泛化测试验证了本方法的鲁棒性。总体而言，EKA推动了迭代式RAG系统的最新技术发展，同时阐明了在强化学习增强框架中结构化推理与高效探索之间的关键相互作用。代码发布于\href{https://github.com/yxzwang/EarlyKnowledgeAlignment}{Github}。