Trust Region On-Policy Distillation
On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent learning, multi-task enhancement, and model compression. However, OPD training becomes unstable when the teacher and student distributions differ substantially, as teacher supervision on student-generated tokens may yield unreliable policy gradients and even cause optimization failure. This work addresses reliable on-policy token-level supervision through credit assignment strategies, and proposes Trust Region On-Policy Distillation, TrOPD. It features the following characteristics: 1) Trust-Region On-Policy Learning: TrOPD performs OPD only...
| Authors | Xingrun Xing, Haoqing Wang, Boyan Gao et al. |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 17 June 2026 |
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