R1-Ranker: Teaching LLM Rankers to Reason

• URL: http://arxiv.org/abs/2506.21638v3
• Date: Thu, 16 Oct 2025 04:41:42 GMT
• Title: R1-Ranker: Teaching LLM Rankers to Reason
• Authors: Tao Feng, Zhigang Hua, Zijie Lei, Yan Xie, Shuang Yang, Bo Long, Jiaxuan You,
• Abstract summary: R1-Ranker is a reasoning-incentive framework built on reinforcement learning.<n>IRanker decomposes ranking into an iterative elimination process with step-wise rewards to encourage deeper reasoning.<n>We evaluate unified R1-Rankers on nine datasets spanning recommendation, routing, and passage ranking.
• Score: 35.35360001710222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have recently shown strong reasoning abilities in domains like mathematics, coding, and scientific problem-solving, yet their potential for ranking tasks, where prime examples include retrieval, recommender systems, and LLM routing, remains underexplored. Ranking requires complex reasoning across heterogeneous candidates, but existing LLM-based rankers are often domain-specific, tied to fixed backbones, and lack iterative refinement, limiting their ability to fully exploit LLMs' reasoning potential. To address these challenges, we propose R1-Ranker, a reasoning-incentive framework built on reinforcement learning, with two complementary designs: DRanker, which generates full rankings in one shot, and IRanker, which decomposes ranking into an iterative elimination process with step-wise rewards to encourage deeper reasoning. We evaluate unified R1-Rankers on nine datasets spanning recommendation, routing, and passage ranking, showing that IRanker-3B consistently achieves state-of-the-art performance, surpasses larger 7B models on some tasks, and yields a 15.7% average relative improvement. Ablation and generalization experiments further confirm the critical role of reinforcement learning and iterative reasoning, with IRanker-3B improving zero-shot performance by over 9% on out-of-domain tasks and reasoning traces boosting other LLMs by up to 22.87%. These results demonstrate that unifying diverse ranking tasks with a single reasoning-driven foundation model is both effective and essential for advancing LLM reasoning in ranking scenarios.

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