arXiv:2605.27028v1 Announce Type: new Abstract: On-policy distillation has recently emerged as a promising alternative to standard sequence-level imitation, training a student by scoring its own rollouts with a teacher model. However, we observe ``Off-policy Teacher Decay'' problem in this paradigm: for the later tokens, with student's earlier trajectory as context that is off-policy to the teacher, the teacher's ability to produce a corrective score would decay, and may fall back to token-completion behavior learned in the pre-training stage. We empirically verify this problem, and we propose
This research addresses a fundamental challenge (Off-policy Teacher Decay) in on-policy distillation, a promising method for training AI agents, indicating a current focus on refining agent training methodologies.
Improving the efficiency and effectiveness of on-policy distillation directly impacts the development and capabilities of advanced AI agents, making their training more robust and less prone to performance decay.
The proposed 'Early Stopping Rollout' scheme offers a concrete architectural improvement to current on-policy distillation techniques, potentially leading to more reliable and powerful AI models.
- · AI researchers
- · companies developing AI agents
- · sectors adopting advanced AI agents
- · developers using less efficient distillation methods
Refined on-policy distillation leads to more robust and higher-performing AI agents.
Improved agent performance accelerates the deployment of autonomous systems across various industries.
More capable and reliable AI agents could fundamentally alter white-collar workflows and the capabilities of automated decision-making systems.
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