NP-OPD adds negative-policy rollouts to on-policy distillation
The paper introduces Negative-Policy OPD (NP-OPD), which adds a lower-performing, lower-capability “negative policy” at the rollout stage of on-policy distillation (OPD), continuously supplying tokens that the negative policy prefers over the teacher so they stay under teacher supervision during training — without changing the distillation reward formulation. The authors report improvements over OPD across model scales, generation modes, reasoning domains and different OPD variants, and analyses indicating NP-OPD suppresses negative-policy-preferred tokens and moves the student away from the negative policy. The 25-page preprint (7 figures, 24 tables) says code will be released.
Why it matters: It suggests a way to strengthen on-policy distillation by adding a negative reference at rollout time rather than redesigning the reward, which may help when teacher and student distributions barely overlap.
Computer Science > Machine Learning
arXiv:2610.07874 (cs)
[Submitted on 6 Oct 2026]
Title:On-Policy Distillation with Negative-Policy Rollouts
Authors:Jaehui Hwang, Dongyoon Han, Sangdoo Yun, Byeongho Heo
View a PDF of the paper titled On-Policy Distillation with Negative-Policy Rollouts, by Jaehui Hwang and 3 other authors
View PDF HTML (experimental)Abstract:On-policy distillation (OPD) has been widely studied as a post-training method in which a student model obtains token-level supervision from a stronger teacher on its own rollouts. Recent studies have improved OPD through alternative distillation reward formulations and teacher configurations, while the objective of distillation remains centered on mimicking the teacher. However, when a stronger teacher has limited distributional overlap with the student, such positive guidance can provide insufficient learning signals. In this work, we introduce Negative-Policy OPD (NP-OPD), which complements teacher supervision with rollouts from a lower-performing, lower-capability negative policy that serves as a negative reference for the student. Rather than modifying the distillation reward formulation, NP-OPD introduces the negative policy at the rollout stage, continuously supplying tokens preferred by the negative policy over the teacher so that they remain exposed to teacher supervision throughout training. This provides an explicit negative signal through negative-policy rollouts while preserving the positive teacher supervision used in OPD. Through extensive experiments, we show that NP-OPD improves OPD across model scales, generation modes, reasoning domains, and different OPD variants. Furthermore, our analyses show that NP-OPD effectively suppresses tokens preferred by the negative policy over the teacher and moves the student away from the negative policy. These results support our design of introducing negative signals through negative-policy rollouts and provide new insight into the role of the rollout policy in OPD. Code will be available at this https URL.
| Comments: | 25 pages, 7 figures, 24 tables |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.07874 [cs.LG] |
| (or arXiv:2610.07874v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07874 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jaehui Hwang [view email][v1] Tue, 6 Oct 2026 07:25:03 UTC (357 KB)
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