Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive decoding latency. However, transferring a generative LLM's ranking knowledge to a decision mod
DecisionRank uses item-space on-policy distillation to transfer ranking knowledge from generative teachers to efficient decision models, achieving state-of-the-art retrieval performance with significantly faster inference.
Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive decoding latency. However, transferring a generative LLM's ranking knowledge to a decision model remains challenging: the teacher expresses preferences through a ranked list of document ID sequences, while the student determines the ranking using scores from the first-token position. To bridge this gap, we introduce DecisionRank, a decision model trained through item-space on-policy distillation (OPD). Item-space OPD constructs the student's rollout by sorting its document scores into a ranked sequence. A generative teacher takes the resulting ranking list as inputs and provides…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
DecisionRank: On-Policy Distillation for Decision-Model Rerankers was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 10 checks on 2 different days. Its best position was #18 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 4, 01:28 UTC.
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#18 on Hugging Face · Daily papers, on Nov 3, 11:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Nov 3, 2026 and has been on the list on 2 days (10 checks) since.