arXiv:2605.31159v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on prefixes sampled from its own policy while matching a stronger teacher. This addresses the prefix mismatch of offline distillation, but early student rollouts can still be poor, placing teacher supervision on weak or low-quality prefixes. We propose Trust-Region behavior Blending (TRB), a warmup method that replaces the early rollout policy with the closest-to-teacher behavior policy inside a student-centered KL trust region, while keeping the per-prefix reverse-KL OPD loss unchanged. The KL budget
The paper addresses a known limitation in current on-policy distillation methods, which is a significant area of research in reinforcement learning and AI training efficiency.
Improving on-policy distillation can accelerate the training and effectiveness of AI models, particularly in agentic systems, by enhancing student learning from powerful teachers.
This method could lead to more robust and efficient training of AI agents, potentially reducing the computational resources and time required to develop high-performing models.
- · AI/ML researchers
- · AI development platforms
- · Companies deploying AI agents
- · Inefficient AI training methods
- · Developers solely reliant on offline distillation
More widespread adoption of on-policy distillation techniques for advanced AI development.
Accelerated progress in agentic AI capabilities due to more efficient model training.
Reduced barriers to entry for developing complex AI systems as training becomes more manageable.
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Read at arXiv cs.LG