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arXiv Computation and Language By Yanran Wu, Dian Yu, Chongyang Bai, Jieyi Zhang, Rui Liu, Tianyi Chen, Shike Mei, Hang Cui, Frank Shyu, Sandeep Pandey, Luke Simon, Chunwei Liu, Xi Liu

DecisionRank: On-Policy Distillation for Decision-Model Rerankers

Read the original on arXiv Computation and Language →

DecisionRank introduces an on-policy distillation (OPD) method that trains decision models to replicate a generative LLM’s ranking knowledge by converting the student’s document scores into a ranked sequence and using a teacher to provide conditional feedback. The approach combines item‑space OPD with offline ranking supervision, achieving state‑of‑the‑art results on retrieval benchmarks such as TREC DL, BEIR, and BRIGHT. Even with a 1.7B parameter model, DecisionRank outperforms its 27B generative teacher on TREC DL19–23, and at 8B it surpasses the generative listwise counterpart while running 24.5× faster on TREC DL and 16.7× faster on BRIGHT.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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