REARANK: Reasoning Re-ranking Agent via Reinforcement Learning
Le Zhang, Bo Wang, Xipeng Qiu, Siva Reddy, Aishwarya Agrawal
Abstract
We present REARANK, a large language model (LLM)-based listwise reasoning rerank- ing agent. REARANK explicitly reasons be- fore reranking, significantly improving both performance and interpretability. Leveraging reinforcement learning and data augmentation, REARANK achieves substantial improvements over baseline models across popular informa- tion 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 under- score the effectiveness of our approach and highlight how reinforcement learning can en- hance LLM reasoning capabilities in reranking.
BibTeX
@inproceedings{emnlp2025_rearankreasoning,
title = {REARANK: Reasoning Re-ranking Agent via Reinforcement Learning},
author = {Le Zhang and Bo Wang and Xipeng Qiu and Siva Reddy and Aishwarya Agrawal},
booktitle = {EMNLP 2025},
year = {2025}
}