On Policy Distillation Trend 2026

On-policy distillation transfers knowledge from a teacher model to a student model using data generated by the student's current policy. This method enhances reinforcement learning by improving sample efficiency and policy performance. It primarily benefits AI researchers and developers optimizing agents for complex tasks, such as robotics or game playing, requiring stable, real-time learning.