TongSearch-QR: Reinforced Query Reasoning for Retrieval

• URL: http://arxiv.org/abs/2506.11603v2
• Date: Mon, 16 Jun 2025 03:35:12 GMT
• Title: TongSearch-QR: Reinforced Query Reasoning for Retrieval
• Authors: Xubo Qin, Jun Bai, Jiaqi Li, Zixia Jia, Zilong Zheng,
• Abstract summary: We introduce TongSearch QR, a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval.<n>With a novel semi-rule-based reward function, we employ reinforcement learning approaches enabling smaller language models.<n>Experiment results on BRIGHT benchmark show that with BM25 as retrievers, both TongSearch QR-7B and TongSearch QR-1.5B models significantly outperform existing baselines.
• Score: 22.833651162995615
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Traditional information retrieval (IR) methods excel at textual and semantic matching but struggle in reasoning-intensive retrieval tasks that require multi-hop inference or complex semantic understanding between queries and documents. One promising solution is to explicitly rewrite or augment queries using large language models (LLMs) to elicit reasoning-relevant content prior to retrieval. However, the widespread use of large-scale language models like GPT-4 or LLaMA3-70B remains impractical due to their high inference cost and limited deployability in real-world systems. In this work, we introduce TongSearch QR (Previously Known as "TongSearch Reasoner"), a family of small-scale language models for query reasoning and rewriting in reasoning-intensive retrieval. With a novel semi-rule-based reward function, we employ reinforcement learning approaches enabling smaller language models, e,g, Qwen2.5-7B-Instruct and Qwen2.5-1.5B-Instruct, to achieve query reasoning performance rivaling large-scale language models without their prohibitive inference costs. Experiment results on BRIGHT benchmark show that with BM25 as retrievers, both TongSearch QR-7B and TongSearch QR-1.5B models significantly outperform existing baselines, including prompt-based query reasoners and some latest dense retrievers trained for reasoning-intensive retrieval tasks, offering superior adaptability for real-world deployment.

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