arXiv — cs.AI preprintsInternational19 October 2026
Does On-Policy Distillation Need to Complete Every Rollout?
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arXiv:2610.21337v2 Announce Type: replace-cross Abstract: On-policy distillation (OPD) effectively transfers capabilities from a teacher to a student through feedback on student-generated rollouts. However, generating complete student rollouts and computing teacher feedback for every token make OPD computationally expensive. We introduce Stochastic Truncated On-Policy Distillation (STOPD), which accelerates OPD by randomly truncating student rollouts and correcting the resulting sampling bias with blockwise inverse-probability weighting (IPW). At each training step, we sample a stopping positi
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