A Survey of On-Policy Distillation for Large Language Models
As Large Language Models continue to grow in both capability and cost, transferring frontier capabilities into smaller, deployable students has become an important engineering problem, and knowledge distillation remains a common technique for this transfer. The prevailing recipe in industrial pipelines, static imitation of teacher-generated text, carries a structural weakness that grows more severe as tasks become longer and more reasoning-intensive. Because the student is trained on flawless teacher prefixes but generates its own at inference, small errors tend to accumulate into trajectories it has rarely been trained to recover from, and the resulting exposure bias has been shown to sc...
| Authors | Mingyang Song, Mao Zheng |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 18 June 2026 |
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