Self-retiring distillation lets student agents learn from teachers strategically: absorb skills early when helpful, then graduate to independent learning once they've internalized enough knowledge to improve further on their own.
This paper proposes RetireOPD, a training method for AI agents that combines reinforcement learning with knowledge distillation from a teacher model. The key innovation is 'adaptive retirement'—the student agent automatically stops learning from the teacher once it becomes reliable enough, then continues improving on its own.