SAGE: Strategy-Adaptive Generation Engine for Query Rewriting

• URL: http://arxiv.org/abs/2506.19783v2
• Date: Sat, 26 Jul 2025 07:12:26 GMT
• Title: SAGE: Strategy-Adaptive Generation Engine for Query Rewriting
• Authors: Teng Wang, Hailei Gong, Changwang Zhang, Jun Wang,
• Abstract summary: We introduce the Strategy-Adaptive Generation Engine (SAGE), which operationalizes expert-crafted strategies in an reinforcement learning framework.<n>SAGE achieves new state-of-the-art NDCG@10 results, but also uncovers a compelling emergent behavior.<n>Our findings demonstrate that strategy-guided RL, enhanced with nuanced reward shaping, offers a scalable, efficient, and more interpretable paradigm for developing the next generation of robust information retrieval systems.
• Score: 8.941793732446856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query rewriting is pivotal for enhancing dense retrieval, yet current methods demand large-scale supervised data or suffer from inefficient reinforcement learning (RL) exploration. In this work, we first establish that guiding Large Language Models (LLMs) with a concise set of expert-crafted strategies, such as semantic expansion and entity disambiguation, substantially improves retrieval effectiveness on challenging benchmarks, including HotpotQA, FEVER, NFCorpus, and SciFact. Building on this insight, we introduce the Strategy-Adaptive Generation Engine (SAGE), which operationalizes these strategies in an RL framework. SAGE introduces two novel reward shaping mechanisms-Strategic Credit Shaping (SCS) and Contrastive Reward Shaping (CRS)-to deliver more informative learning signals. This strategy-guided approach not only achieves new state-of-the-art NDCG@10 results, but also uncovers a compelling emergent behavior: the agent learns to select optimal strategies, reduces unnecessary exploration, and generates concise rewrites, lowering inference cost without sacrificing performance. Our findings demonstrate that strategy-guided RL, enhanced with nuanced reward shaping, offers a scalable, efficient, and more interpretable paradigm for developing the next generation of robust information retrieval systems.

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