Beyond Recall Saturation: Scaling Hard-Negative Reranker Training for Industrial Legal Retrieval

Industrial retrieval systems often improve quality by enlarging embedding candidate pools or adopting complex training pipelines. We show that, under a sufficiently large but practical candidate pool, embedding recall exhibits diminishing returns, motivating a closer examination of reranker quality.