---
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标题: "ReOrder-OPD:Reliability-Aware Prompt Ordering for On-Policy Distillation"
原文链接: "https://arxiv.org/abs/2608.10905"
发布日期: "2026-08-11"
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去重键: "https://arxiv.org/abs/2608.10905"
---

## Computer Science > Machine Learning

## Title:ReOrder-OPD:Reliability-Aware Prompt Ordering for On-Policy Distillation

Authors:[Ximo Zhu](https://arxiv.org/search/cs?searchtype=author&query=Zhu,+X), [Ruiqi Liu](https://arxiv.org/search/cs?searchtype=author&query=Liu,+R), [Rong Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+R), [Ping Wu](https://arxiv.org/search/cs?searchtype=author&query=Wu,+P), [Xiang Zheng](https://arxiv.org/search/cs?searchtype=author&query=Zheng,+X), [Wenzhuo Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+W), [Xubin Yao](https://arxiv.org/search/cs?searchtype=author&query=Yao,+X), [Zhiyuan Yan](https://arxiv.org/search/cs?searchtype=author&query=Yan,+Z), [Bo Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+B), [Jun Gao](https://arxiv.org/search/cs?searchtype=author&query=Gao,+J), [Xiaolei Lv](https://arxiv.org/search/cs?searchtype=author&query=Lv,+X)

[View PDF](https://arxiv.org/pdf/2608.10905) [HTML (experimental)](https://arxiv.org/html/2608.10905v1)

> Abstract:On-policy distillation (OPD) applies token-level teacher supervision to student-generated trajectories, but this supervision is not always reliable. Existing methods use local confidence or teacher-student agreement to weight, filter, or truncate the sampled trajectory. These signals do not directly determine whether the teacher can continue a student prefix to a correct answer, and trajectory-level interventions can conflate one rollout's unreliability with low expected training value of its prompt. We define prompt-level teacher continuation reliability $R$ as the teacher's probability of reaching a correct answer from a student prefix, averaged over prefixes and trajectories induced by the current student. Oracle experiments show that high-$R$ prompts yield larger OPD gains and that descending-$R$ training outperforms random and ascending orders on a fixed prompt pool. Because estimating $R$ requires many teacher continuations, we use the maximum ROUGE-5 F1 between one independent student rollout and verifier-correct same-prompt teacher trajectories. Across ten equal-frequency bins of this actual score, mean $R$ rises monotonically, showing that the proxy separates coarse reliability levels. ReOrder-OPD sorts prompts by the proxy, then draws independent on-policy training trajectories for vanilla OPD. It improves every matched aggregate comparison across Qwen3 and Gemma4 mathematics settings and Qwen3 code settings. Gains in all six FiRe-OPD and ExOPD settings show that prompt ordering complements within-trajectory supervision.

| Subjects: | Machine Learning (cs.LG) |
| --- | --- |
| Cite as: | [arXiv:2608.10905](https://arxiv.org/abs/2608.10905) \[cs.LG\] |
|  | (or [arXiv:2608.10905v1](https://arxiv.org/abs/2608.10905v1) \[cs.LG\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10905](https://doi.org/10.48550/arXiv.2608.10905) |

## Submission history

From: Ximo Zhu \[[view email](https://arxiv.org/show-email/3fa69a2d/2608.10905)\]  
**\[v1\]** Tue, 11 Aug 2026 13:27:56 UTC (706 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10905) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
