When Is a Reranker Worth It?

Rerankers should be evaluated only after verifying that relevant documents are present in the retrieval candidate set and establishing a labeled nDCG@10 baseline against the strongest first-stage ranking, preferably tuned hybrid fusion rather than default reciprocal rank fusion. Cross-encoders can improve ordering by jointly reading queries and documents, but their query-time cost limits them to reranking a small candidate list; tests should begin with 10 candidates, use the intended production model, and validate gains on held-out queries before increasing depth. Results across five datasets showed that model fit, particularly context-window length and training-domain alignment, mattered more than most configuration choices: jina-reranker-v2 produced confirmed gains on CodeSearchNet and DBPedia-entity, while several apparent gains did not survive held-out validation and WANDS remained better served by tuned fusion. Larger candidate pools help only when they continue adding relevant documents that the first stage ranked too low, while excessive depth can hurt quality and substantially increase latency. Production decisions should balance validated relevance improvements against throughput, document length, hardware, model licensing, and tail latency, while alternative stages such as maximal marginal relevance, grouping, formula-based rescoring, or late-interaction models may better address diversity, duplicate chunks, payload-based ranking, or cross-encoder speed constraints.

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