[2605.12652] Multi-Rollout On-Policy Distillation via Peer Successes and Failures
[Submitted on 12 May 2026]
Title:Multi-Rollout On-Policy Distillation via Peer Successes and Failures
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Abstract:Large language models are often post-trained with sparse verifier rewards, which indicate whether a sampled trajectory succeeds but provide limited guidance about where reasoning succeeds or fails. On-policy distillation (OPD) offers denser token-level supervision by training on student-generated trajectories, yet existing methods typically distill each rollout independently and ignore the other attempts sampled for the same prompt. We introduce Multi-Rollout On-Policy Distillation (MOPD), a peer-conditioned distillation framework that uses the student's local rollout group to construct more informative teacher signals. MOPD conditions the teacher on both successful and failed peer rollouts: successes provide positive evidence for valid reasoning patterns, while failures provide structured negative evidence about plausible mistakes to avoid. We study two peer-context constructions: positive peer imitation and contrastive success-failure conditioning. Experiments on competitive programming, mathematical reasoning, scientific question answering, and tool-use benchmarks show that MOPD consistently improves over standard on-policy baselines. Further teacher-signal analysis shows that mixed success-failure contexts better align teacher scores with verifier rewards, indicating that the gains arise from more faithful, instance-adaptive supervision. These results indicate that effective on-policy distillation should exploit the student's multi-rollout trial-and-error behavior rather than treating rollouts as isolated samples.
Comments: 23 pages
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.12652 [cs.LG]
(or arXiv:2605.12652v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.12652
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Weichen Yu [view email] [v1] Tue, 12 May 2026 18:57:44 UTC (454 KB)
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