Rethinking the Reranker: Boundary-Aware Evidence Selection for Robust Retrieval-Augmented Generation

TL;DR

This paper introduces BAR-RAG, a boundary-aware evidence selector for retrieval-augmented generation that improves robustness and performance under noisy retrieval conditions by focusing on evidence that is optimally challenging for the generator.

Contribution

BAR-RAG redefines the reranker as a boundary-aware evidence selector trained with reinforcement learning to target the generator's optimal evidence, enhancing robustness and accuracy.

Findings

Achieves 10.3% average performance gain over baselines.

Significantly improves robustness under retrieval noise.

Effectively targets evidence that balances difficulty and sufficiency.

Abstract

Retrieval-Augmented Generation (RAG) systems remain brittle under realistic retrieval noise, even when the required evidence appears in the top-K results. A key reason is that retrievers and rerankers optimize solely for relevance, often selecting either trivial, answer-revealing passages or evidence that lacks the critical information required to answer the question, without considering whether the evidence is suitable for the generator. We propose BAR-RAG, which reframes the reranker as a boundary-aware evidence selector that targets the generator's Goldilocks Zone -- evidence that is neither trivially easy nor fundamentally unanswerable for the generator, but is challenging yet sufficient for inference and thus provides the strongest learning signal. BAR-RAG trains the selector with reinforcement learning using generator feedback, and adopts a two-stage pipeline that fine-tunes the generator under the induced evidence distribution to mitigate the distribution mismatch between training and inference. Experiments on knowledge-intensive question answering benchmarks show that BAR-RAG consistently improves end-to-end performance under noisy retrieval, achieving an average gain of 10.3 percent over strong RAG and reranking baselines while substantially improving robustness. Code is publicly avaliable at https://github.com/GasolSun36/BAR-RAG.