title: REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
summary: We present REARANK, a large language model (LLM)-based listwise reasoning
  reranking agent. REARANK explicitly reasons before reranking, significantly
  improving both performance and interpretability. Leveraging reinforcement
  learning and data augmentation, REARANK achieves substantial improvements over
  baseline models across popular information retrieval benchmarks, notably
  requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our
  REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and
  out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT
  benchmarks. These results underscore the effectiveness of our approach and
  highlight how reinforcement learning can enhance LLM reasoning capabilities in
  reranking.
paper_authors._id: 6835ca4b375575936dca6572
paper_authors.name: Le Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T15:48:59.328Z
paper_authors._id: 633b423e5df91da9ceafe40a
paper_authors.avatarUrl: /avatars/6d8c31c5f3e64d8a085b6e1228bcc44d.svg
paper_authors.fullname: le.zhang
paper_authors.type: user
paper_authors.user: le723z
paper_authors._id: 6835ca4b375575936dca6573
paper_authors.name: Bo Wang
paper_authors._id: 6835ca4b375575936dca6574
paper_authors.name: Xipeng Qiu
paper_authors._id: 6835ca4b375575936dca6575
paper_authors.name: Siva Reddy
paper_authors._id: 6835ca4b375575936dca6576
paper_authors.name: Aishwarya Agrawal
paper_title: REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
paper_summary: We present REARANK, a large language model (LLM)-based listwise reasoning
  reranking agent. REARANK explicitly reasons before reranking, significantly
  improving both performance and interpretability. Leveraging reinforcement
  learning and data augmentation, REARANK achieves substantial improvements over
  baseline models across popular information retrieval benchmarks, notably
  requiring only 179 annotated samples. Built on top of Qwen2.5-7B, our
  REARANK-7B demonstrates performance comparable to GPT-4 on both in-domain and
  out-of-domain benchmarks and even surpasses GPT-4 on reasoning-intensive BRIGHT
  benchmarks. These results underscore the effectiveness of our approach and
  highlight how reinforcement learning can enhance LLM reasoning capabilities in
  reranking.
paper_ai_summary: REARANK, a reinforcement learning-enhanced large language model for listwise reasoning,outperforms baseline models and even surpasses GPT-4 on reasoning-intensive benchmarks with minimal data.
paper_ai_keywords: large language model listwise reasoning reinforcement learning data augmentation reranking information retrieval benchmarks BRIGHT benchmarks