A new method called RetireOPD proposes self-retiring on-policy distillation to give multi-turn reinforcement learning agents dense token-level supervision from a self-teacher with privileged task skills, so a skill-free student can internalize them. The approach targets the sparse single scalar reward per trajectory that multi-turn RL agents receive. The source provides no further results, figures, or named organizations.
Multi-turn agents trained with reinforcement learning (RL) receive a single scalar reward per trajectory, which motivates self on-policy distillation (OPD) to supply dense token-level supervision from a self-teacher with privileged task skills, letting a skill-free student internalize them. This rec
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