On the Position Bias of On-Policy Distillation
On-Policy Distillation (OPD) improves the learning efficiency of standard reinforcement learning through dense, token-level supervision from teachers. In the standard KL objective of OPD, token-level losses are uniformly averaged, implying equal weights for all tokens. However, we discover that not all tokens are created equal: as student rollouts grow longer, they deviate further from the teacher's distribution, leading to degraded supervision quality at later positions. As a result, OPD using only the first 30% of tokens can perform comparably to using all tokens, whereas OPD using only the last 30% of tokens barely learns anything. In this work, we provide a principled understanding of...
| Authors | Yan Xie, Sijie Zhu, Tiansheng Wen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 26 June 2026 |
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