ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability
Wenhan Liu, Xinyu Ma, Weiwei Sun, Yutao Zhu, Yuchen Li, Dawei Yin, Zhicheng Dou · 2026
Large Language Model (LLM) based listwise ranking has shown superior performance in many passage ranking tasks.With the development of Large Reasoning Models (LRMs), many studies have demonstrated that step-bystep reasoning during test-time helps improve listwise ranking performance.However, due to the scarcity of reasoning-intensive training data, existing rerankers perform poorly in many complex ranking scenarios, and the ranking ability of reasoning-intensive rerankers remains largely underdeveloped.In this paper, we first propose an automated reasoning-intensive training data synthesis framework, which sources training queries and passages from diverse domains and applies DeepSeek-R1 to generate high-quality training labels.To empower the listwise reranker with strong reasoning ability, we further propose a two-stage training approach, which includes a cold-start supervised fine-tuning (SFT) stage and a reinforcement learning (RL) stage.During the RL stage, we design a novel multi-view ranking reward tailored to the multi-turn nature of listwise ranking.Extensive experiments demonstrate that our trained reasoning-intensive reranker Rea-sonRank outperforms existing baselines significantly and also achieves much lower latency than the pointwise reranker.