Multi-hop Reasoning via Early Knowledge Alignment
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 https://github.com/yxzwang/EarlyKnowledgeAlignment{Github}.
694bd0b2746a34b55dd53f59
Yuxin Wang
694bd0b2746a34b55dd53f5a
Shicheng Fang
694bd0b2746a34b55dd53f5b
Bo Wang
claimed_verified
2026-04-15T12:10:26.875Z
663c23b4a4152b77b67b7c1c
/avatars/7af03530ce2b48bc77ce84296878978f.svg
Bo Wang
Musicode
user
Musicode
694bd0b2746a34b55dd53f5c
Qi Luo
694bd0b2746a34b55dd53f5d
Xuanjing Huang
694bd0b2746a34b55dd53f5e
Yining Zheng
694bd0b2746a34b55dd53f5f
Xipeng Qiu
Multi-hop Reasoning via Early Knowledge Alignment
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 https://github.com/yxzwang/EarlyKnowledgeAlignment{Github}.
Early Knowledge Alignment improves retrieval and reasoning in iterative RAG systems by aligning LLMs with relevant knowledge before planning, enhancing performance and efficiency.
Retrieval-Augmented Generation Large Language Models iterative RAG reinforcement learning multi-hop questions retrieval set contextually relevant retrieved knowledge retrieval precision cascading errors entropy perspective efficient exploration structured reasoning