Xiuyu Li(@sheriyuo):OPID does skill distillation for agentic RL using the agent's own completed trajectories as the skill source, so the supervision stays on-policy and matched to the current state distribution. Hand-written or off-policy skill libraries drift from what the policy actually visits in multi-turn interaction, which is the gap it targets. Skills are extracted in hindsight from finished rollouts and split into two levels: episode-level skills that capture global workflows and failure-avoidance rules, and step-level skills for local decisions, with a critical-first routing that distills the high-leverage ones back into the policy. OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning Paper:

2026.06.26 06:13

OPID does skill distillation for agentic RL using the agent's own completed trajectories as the skill source, so the supervision stays on-policy and matched to the current state distribution. Hand-written or off-policy skill libraries drift from what the policy actually visits in multi-turn interaction, which is the gap it targets. Skills are extracted in hindsight from finished rollouts and split into two levels: episode-level skills that capture global workflows and failure-avoidance rules, and step-level skills for local decisions, with a critical-first routing that distills the high-leverage ones back into the policy. OPID: On-Policy Skill Distillation for Agentic Reinforcement Learning Paper: