{"slug": "retireopd-self-retiring-on-policy-distillation-for-agentic-reinforcement", "title": "RetireOPD: Self-Retiring On-Policy Distillation for Agentic Reinforcement Learning", "summary": "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.", "body_md": "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", "url": "https://wpnews.pro/news/retireopd-self-retiring-on-policy-distillation-for-agentic-reinforcement", "canonical_source": "https://aiflash.com/news/121751/", "published_at": "2026-09-18 03:00:08+00:00", "updated_at": "2026-09-18 03:24:54.250108+00:00", "lang": "en", "topics": ["ai-agents", "machine-learning", "ai-research"], "entities": ["RetireOPD"], "alternates": {"html": "https://wpnews.pro/news/retireopd-self-retiring-on-policy-distillation-for-agentic-reinforcement", "markdown": "https://wpnews.pro/news/retireopd-self-retiring-on-policy-distillation-for-agentic-reinforcement.md", "text": "https://wpnews.pro/news/retireopd-self-retiring-on-policy-distillation-for-agentic-reinforcement.txt", "jsonld": "https://wpnews.pro/news/retireopd-self-retiring-on-policy-distillation-for-agentic-reinforcement.jsonld"}}