Make Reranking a Training Problem, Not a Production Tax
TL;DR for operators A RAG system can retrieve highly relevant passages and still train its generator on poor learning examples. Evidence that makes the answer nearly automatic may encourage shortcuts; evidence that leaves the answer effectively unreachable provides little usable training signal. BAR-RAG1 tests a different objective: keep evidence relevant, but prefer training sets on which the current generator succeeds roughly half the time. The selector is used to construct that training distribution, the generator is adapted to it, and the selector is then removed. Across three backbones, best-iteration BAR-RAG improves average exact match over standard RAG by 7.3, 12.2, and 11.6 points respectively. Against the stronger RAG-SFT baseline, the gains narrow to 3.9, 7.3, and 6.2 points. ...