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17 SEP 2026 · Paper

RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning

This paper proposes a new method for self-retiring on-policy distillation in agentic reinforcement learning, which allows agents to learn more effectively by switching between teacher-student training and reinforcement learning alone. Practitioners might care about this approach because it can improve the performance of agents in complex tasks.

agentic reinforcement learning self-supervised learning on-policy distillation continual learning adaptive methods

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RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning