TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
On-policy distillation (OPD) trains a student policy by matching a stronger teacher on the student's own trajectories, offering a promising framework for language agent training. However, its application to long-horizon agentic tasks remains insufficiently explored. We identify two key inefficiencies in vanilla agent OPD: (1) full-horizon rollouts often waste wall-clock resources on tail turns that provide weak and noisy KL supervision, and (2) trajectory-level KL objectives concentrate most of the loss on shallow tokens, leaving deeper decision turns under-trained once initial behaviors are aligned. To address these challenges, we propose TurnOPD, a turn-level budgeting strategy for effi...
| Authors | Yuhang Zhou, Kai Zheng, Haoling Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 July 2026 |
| Last updated at source | 28 September 2026 |
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