ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability
Large Language Model (LLM) based listwise ranking has shown superior
performance in many passage ranking tasks. With the development of Large
Reasoning Models, many studies have demonstrated that step-by-step 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. A
self-consistency data filtering mechanism is designed to ensure the data
quality. To empower the listwise reranker with strong reasoning ability, we
further propose a two-stage post-training approach, which includes a cold-start
supervised fine-tuning (SFT) stage for reasoning pattern learning and a
reinforcement learning (RL) stage for further ranking ability enhancement.
During the RL stage, based on the nature of listwise ranking, we design a
multi-view ranking reward, which is more effective than a ranking metric-based
reward. Extensive experiments demonstrate that our trained reasoning-intensive
reranker ReasonRank outperforms existing baselines significantly and
also achieves much lower latency than pointwise reranker Rank1. Through
further experiments, our ReasonRank has achieved state-of-the-art (SOTA)
performance 40.6 on the BRIGHT
leaderboard\footnote{https://brightbenchmark.github.io/.} Our codes are
available at https://github.com/8421BCD/ReasonRank.
689ab8b6fab6fdd2e52ac4a2
Wenhan Liu
689ab8b6fab6fdd2e52ac4a3
Xinyu Ma
claimed_verified
2025-08-13T07:20:03.468Z
62cd2f13979d883655cd5377
/avatars/400c252d20d68aca56e0d0280498ce17.svg
Xinyu Ma
user
xyma
689ab8b6fab6fdd2e52ac4a4
Weiwei Sun
689ab8b6fab6fdd2e52ac4a5
Yutao Zhu
689ab8b6fab6fdd2e52ac4a6
Yuchen Li
689ab8b6fab6fdd2e52ac4a7
Dawei Yin
689ab8b6fab6fdd2e52ac4a8
Zhicheng Dou
ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability
Large Language Model (LLM) based listwise ranking has shown superior
performance in many passage ranking tasks. With the development of Large
Reasoning Models, many studies have demonstrated that step-by-step 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. A
self-consistency data filtering mechanism is designed to ensure the data
quality. To empower the listwise reranker with strong reasoning ability, we
further propose a two-stage post-training approach, which includes a cold-start
supervised fine-tuning (SFT) stage for reasoning pattern learning and a
reinforcement learning (RL) stage for further ranking ability enhancement.
During the RL stage, based on the nature of listwise ranking, we design a
multi-view ranking reward, which is more effective than a ranking metric-based
reward. Extensive experiments demonstrate that our trained reasoning-intensive
reranker ReasonRank outperforms existing baselines significantly and
also achieves much lower latency than pointwise reranker Rank1. Through
further experiments, our ReasonRank has achieved state-of-the-art (SOTA)
performance 40.6 on the BRIGHT
leaderboard\footnote{https://brightbenchmark.github.io/.} Our codes are
available at https://github.com/8421BCD/ReasonRank.
A reasoning-intensive reranker, ReasonRank, achieves state-of-the-art performance in passage ranking tasks by using synthesized training data and a two-stage post-training approach with reinforcement learning.
Large Language Model listwise ranking Large Reasoning Models step-by-step reasoning DeepSeek-R1 self-consistency data filtering cold-start supervised fine-tuning reinforcement learning multi-view ranking reward BRIGHT leaderboard