Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement
On-policy distillation (OPD) offers dense token-level supervision as an alternative to the sparse outcome-level advantages of reinforcement learning with verifiable rewards (RLVR). However, the teacher scores student-generated trajectories that are inherently off-policy for it, so the reliability of its supervision, and hence the source of the student's improvement, remains unclear. We quantitatively analyze teacher supervision during OPD training and find substantial noise whose prevalence increases with teacher scale. Surprisingly, the student policy is insensitive to such noise, converging to comparable performance regardless of whether noisy supervision is retained or removed. Does OP...
| Authors | Yi Ding, Ruqi Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 31 August 2026 |
| Last updated at source | 24 September 2026 |
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