# DecisionRank: On-Policy Distillation for Decision-Model Rerankers

**Authors:** 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  
**Lab:** Meta · **Published:** 29 Oct 2026 · **Upvotes:** 14  
**Links:** [arXiv](https://arxiv.org/abs/2610.42128) · [PDF](https://arxiv.org/pdf/2610.42128) · [This page](https://omen-bit.github.io/ai-paper-daily/paper/2610.42128.html)

## Key numbers

- **1.7B**: Even at 1.7B parameters

## 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 24.5times faster on TREC DL and 16.7times faster on BRIGHT.

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Notes written by AI from the paper, numbers copied from the abstract. Check the paper before you rely on them. From AI Papers Daily.