Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typically optimized on static human annotated relevance labels in isolation, decoupled from the downstream generation process. This isolation leads to a fundamental misalignment: documents identified as topically relevant by information retrieval metrics often fail to provide the actual utility required by the LLM for precise answer generation. To bridge this gap, we introduce ReRanking Preference Optimization (RRPO), a reinforcement learning framework that directly aligns reranking with the LLM's generation quality. By formulating reranking as a sequential...
| Authors | Yuhang Wu, Xiangqing Shen, Fanfan Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 April 2026 |
| Last updated at source | 2 July 2026 |
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