REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

le.zhang, Bo Wang, Xipeng Qiu, Siva Reddy, Aishwarya Agrawal

REARANK: Reasoning Re-ranking Agent via Reinforcement Learning: 18 upvotes on Hugging Face Daily Papers, #21 of 71 papers on 2025-05-27. Day-by-day upvote history.

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 page on Hugging Face · arXiv

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