Teacher Should Think Ahead: Adaptive Continuations for Reliable On-Policy Distillation

Abstract:On-policy distillation (OPD) is a promising approach for transferring knowledge between language models, where a student receives dense token-level supervision along its own generated trajectories. However, teacher supervision can be unreliable when conditioned on incomplete or low-quality student prefixes. We identify Teacher Uncertainty Contraction (TUC), a systematic phenomenon whereby the teacher's predictive uncertainty decreases as it continues from a student-generated prefix. We theoretically characterize this trade-off through a variance-bias decomposition of teacher-branch gradients, showing that uncertainty contraction reduces variance while teacher-student path divergence increases bias, thereby favoring a finite continuation. Guided by this insight, we propose Adaptive-Continuations On-Policy Distillation (AC-OPD), which augments informative states along student rollouts with teacher continuations and adaptively selects their effective supervision horizons. Experiments on mathematical reasoning and code generation across model scales demonstrate that AC-OPD consistently improves over standard OPD. Controlled-continuations and matched-budget analyses further validate the adaptive-continuations design, highlighting adaptive teacher continuations as an effective principle for reliable on-policy this http URL code will be made publicly available upon publication.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.22254 [cs.LG]

(or arXiv:2609.22254v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22254 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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