Generalized Reinforcement Learning for Retriever-Specific Query Rewriter with Unstructured Real-World Documents

• URL: http://arxiv.org/abs/2507.23242v1
• Date: Thu, 31 Jul 2025 04:55:21 GMT
• Title: Generalized Reinforcement Learning for Retriever-Specific Query Rewriter with Unstructured Real-World Documents
• Authors: Sungguk Cha, DongWook Kim, Taeseung Hahn, Mintae Kim, Youngsub Han, Byoung-Ki Jeon,
• Abstract summary: textbfRL-QR is a reinforcement learning framework for retriever-specific query rewriting.<n> RL-QR trains query rewriters tailored to specific retrievers, enhancing retrieval performance across varied domains.<n>Our findings highlight RL-QR's potential to revolutionize query optimization for RAG systems.
• Score: 4.200973008100858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) systems rely heavily on effective query formulation to unlock external knowledge, yet optimizing queries for diverse, unstructured real-world documents remains a challenge. We introduce \textbf{RL-QR}, a reinforcement learning framework for retriever-specific query rewriting that eliminates the need for human-annotated datasets and extends applicability to both text-only and multi-modal databases. By synthesizing scenario-question pairs and leveraging Generalized Reward Policy Optimization (GRPO), RL-QR trains query rewriters tailored to specific retrievers, enhancing retrieval performance across varied domains. Experiments on industrial in-house data demonstrate significant improvements, with $\text{RL-QR}_{\text{multi-modal}}$ achieving an 11\% relative gain in NDCG@3 for multi-modal RAG and $\text{RL-QR}_{\text{lexical}}$ yielding a 9\% gain for lexical retrievers. However, challenges persist with semantic and hybrid retrievers, where rewriters failed to improve performance, likely due to training misalignments. Our findings highlight RL-QR's potential to revolutionize query optimization for RAG systems, offering a scalable, annotation-free solution for real-world retrieval tasks, while identifying avenues for further refinement in semantic retrieval contexts.

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