标题： REARANK：通过强化学习进行推理重新排序的代理 显示英文标题

标题： REARANK: Reasoning Re-ranking Agent via Reinforcement Learning

摘要： 我们提出REARANK，一个基于大型语言模型（LLM）的列表推理重新排序代理。 REARANK在重新排序之前显式地进行推理，显著提高了性能和可解释性。 利用强化学习和数据增强，REARANK在流行的信息检索基准测试中相对于基线模型取得了显著改进，特别是在仅需要179个标注样本的情况下。 基于Qwen2.5-7B构建，我们的REARANK-7B在领域内和领域外基准测试中的表现与GPT-4相当，甚至在推理密集型的BRIGHT基准测试中超越了GPT-4。 这些结果证明了我们方法的有效性，并突显了强化学习如何在重新排序中增强LLM的推理能力。 显示英文摘要

摘要： 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.