| title | Trust Region On-Policy Distillation |
|---|---|
| authors | Xingrun Xing, Haoqing Wang, Boyan Gao, Ziheng Li, Yehui Tang |
| date | 2026-05-31 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.01249 |
| affiliation | Samsung Research |
| upvotes | 46 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | 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 in regions where the teacher provides reliable supervision, mitigating the optimization difficulty of the K1 reverse-KL estimator under distribution mismatch. 2) Outlier Estimation: For outlier regions, we explore gradient clipping, masking, and forward-KL estimation to reduce the adverse effects of unreliable supervision. 3) Off-Policy Guidance: The student continues generation from teacher prefixes and uses forward KL to imitate off-policy guidance, encouraging on-policy exploration toward reliable regions. Experiments show that TrOPD consistently outperforms SoTA OPD baselines, including OPD, EOPD, and REOPOLD, across mathematical reasoning, code generation, and general-domain benchmarks. |
cite
@misc{xing2026trust,
title={Trust Region On-Policy Distillation},
author={Xingrun Xing and Haoqing Wang and Boyan Gao and Ziheng Li and Yehui Tang},
year={2026},
eprint={2606.01249},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.01249}
}