RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning
Quick summary
arXiv:2609.20784v1 Announce Type: cross Abstract: 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 recipe, however, is undermined by two findings in agentic tasks: privileged information alone does not always make a teacher reliable, and the benefit of teacher supervision is stage-dependent. We therefore propose RetireOPD (Self-Retiring On-Policy Dis
Key takeaways
- arXiv:2609.20784v1 Announce Type: cross Abstract: 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 recipe, however, is undermined by two findings in agentic tasks: privileged information alone does not always make a teacher reliable, and the benefit of teacher supervision is stage-dependent.
- We therefore propose RetireOPD (Self-Retiring On-Policy Dis
Why it matters
The significance is not only the legal text but how it changes product design. Decisions around “RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning” may reshape data collection, model training, output accountability and market access.
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