StageRAG: Answer-Supervised Cascade Optimization for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) is commonly deployed as a cascade: a retriever recalls candidates, a reranker selects a compact context, and a language model generates the answer. Optimizing this cascade from answer feedback alone faces two challenges. Corpus-specific supporting chains are costly and ambiguous to annotate, creating a Gold-Evidence Bottleneck; meanwhile, final-answer feedback does not reveal where evidence was lost, creating Cascade Credit Ambiguity. We introduce StageRAG, an answer-supervised framework that improves both the retriever and reranker without gold supporting-document annotations. StageRAG records candidate progression in a logged RAG execution, uses an answer-and-citation verifier to determine answer correctness and support attribution, converts the resulting states into query-level preferences, and balances them with per-query caps. We also formalize a pipeline-aware ranking objective, derive a pointwise rule under idealized conditions, and bound its regret under bounded evidence interactions. On HotpotQA, 2WikiMultiHopQA, and TAT-QA, StageRAG improves F1 over the strongest compared answer-supervised baseline by 5.65, 12.44, and 6.43 points, respectively. Cascade diagnostics show improved alignment with downstream trajectory preferences and recovery of supporting evidence into the final context.