arXiv:2606.24084v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student policy using teacher signals computed on trajectories sampled by the student itself. Recent work shows that sampled-token OPD can be fragile on long-horizon reasoning tasks and that local teacher-support matching is a simple and effective repair. This paper introduces blockwise policy-drift gating, a lightweight student-only old-current drift controller for OPD under rollout reuse. The method computes log-probability shifts between the behavior student and the current student on the sampled token pa
The paper addresses known fragility issues in on-policy distillation techniques for AI agents, proposing a solution relevant as AI systems tackle increasingly complex, long-horizon tasks.
Improved on-policy distillation methods can lead to more robust, efficient, and capable AI agents, accelerating their deployment in complex real-world scenarios.
The proposed 'blockwise policy-drift gating' method offers a more stable training approach for student policies, potentially reducing training instability and improving performance in agentic systems.
- · AI agents developers
- · Robotics
- · AI research institutions
- · Companies deploying complex AI systems
- · Inefficient AI training methods
- · Applications demanding high reliability from fragile models
More stable and performant on-policy distillation for AI models.
Faster development and deployment of complex AI agents in various industries.
Enhanced automation capabilities across sectors as reliable agentic systems become more feasible.
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Read at arXiv cs.AI