arXiv — cs.AI preprintsInternational9 October 2026
Stochastic Teacher Intervention for Agentic On-Policy Distillation
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arXiv:2610.10878v1 Announce Type: cross Abstract: On-policy distillation (OPD) efficiently transfers capabilities from a stronger teacher to a student language model through dense token-level supervision on student-generated rollouts and has shown promise on complex tasks such as mathematical reasoning. However, in multi-turn agentic tasks, student decisions shape subsequent observations, causing early errors to accumulate across turns. The resulting trajectories can drift away from the teacher's rollout distribution, making the teacher's token-level supervision less reliable or even counterpr
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