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The paper proposes a training method that distills a large generative LLM's ranking knowledge into a much smaller, faster scoring model, using on-policy distillation over ranked document lists. Its headline claims are that a 1.7B student beats its 27B teacher on TREC DL19–23 and that an 8B version runs 16–25x faster than its listwise counterpart. These are benchmark results from a single arXiv preprint, so readers should check reproducibility and whether the gains hold outside the evaluated retrieval sets. The approach matters most for teams trying to cut retrieval latency without giving up LLM-level ranking quality.
DecisionRank: On-Policy Distillation for Decision-Model Rerankers
arXiv:2610.42128v1 Announce Type: new Abstract: 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 conditional feedback to directly supervise those scores. Combined with offline ranking supervision, DecisionRank achieves state-of-the-art performance on both general and reasoning-intensive retrieval benchmarks such as TREC DL, BEIR, and BRIGHT. Across four benchmarks, item-space OPD consistently outperforms alternative training objectives. Even at 1.7B parameters, DecisionRank surpasses its 27B generative teacher on TREC DL19--23. At 8B, it outperforms its generative listwise counterpart while running $24.5\times$ faster on TREC DL and $16.7\times$ faster on BRIGHT.
Abstract: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 conditional feedback to directly supervise those scores. Combined with offline ranking supervision, DecisionRank achieves state-of-the-art performance on both general and reasoning-intensive retrieval benchmarks such as TREC DL, BEIR, and BRIGHT. Across four benchmarks, item-space OPD consistently outperforms alternative training objectives. Even at 1.7B parameters, DecisionRank surpasses its 27B generative teacher on TREC DL19--23. At 8B, it outperforms its generative listwise counterpart while running $24.5\times$ faster on TREC DL and $16.7\times$ faster on BRIGHT.
Computation and Language (cs.CL)
arXiv:2610.42128 [cs.CL]
(or arXiv:2610.42128v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.42128 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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