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Matching papers

Waypoint-OPD: Transferring RL-Induced Policy Shifts via Gated On-Policy Distillation

667 upvotes · 15 OCT 2026 · Bingxi Hou, Guochao Jiang, Guofeng Quan et al.

This paper develops a method to transfer policy shifts from a reinforcement learning (RL) teacher to a student model, using an intermediate teacher checkpoint to compare early and late policy shifts and gate the endpoint shift based on their agreement. Practitioners in RL and reinforcement learning may care about this paper because it proposes a more effective way to transfer policy shifts, which is crucial for improving student performance in various domains.

reinforcement learningpolicy distillationon-policy distillationpolicy transfergated distillation

Does On-Policy Distillation Really Distill? From Noisy Teacher to Self-Improvement

89 upvotes · 31 AUG 2026 · Yi Ding, Ruqi Zhang

This paper investigates whether on-policy distillation (OPD) truly improves student policies by analyzing the effects of noisy teacher supervision. It finds that OPD works by suppressing low-probability tokens, which can be achieved without a teacher, and introduces a new method called On-Policy Self-Adaptation (OPSA) that outperforms OPD and traditional reinforcement learning methods.

reinforcement learningon-policy methodsself-supervised learningpolicy distillationcontinual learning