Data-Free On-Policy Distillation: How Far Can We Go Without External Data?
On-policy distillation (OPD) is increasingly applied to frontier foundation model post-training. Prior work in this area has largely focused on algorithmic advances, yet it remains unclear how much OPD depends on its training questions and, in particular, how far this dependence can be reduced. Across two representative single-teacher OPD settings, we find that training on 8 real prompts yields performance comparable to training on 17k problems, while datasets differing substantially in measured difficulty and initial distillation gap yield similar outcomes. Our analyses suggest two complementary explanations: repeated sampling could allow even a few prompts to expose substantial teacher ...
| Authors | Gengsheng Li, Mao Zheng, Mingyang Song et al. |
|---|---|
| Primary category | cs.LG |
| Published | 12 September 2026 |
| Last updated at source | 7 October 2026 |
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