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AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning

85 upvotes · 6 AUG 2026 · Zi-Han Wang, Zhengxi Lu, Zhiyuan Yao et al.

This paper proposes a method called AgentOPSD for turn-level credit assignment in agentic reinforcement learning, which helps to identify pivotal decisions that determine outcomes in long-horizon tasks. Practitioners may care about this because it can improve the performance of reinforcement learning agents in complex tasks.

agentic reinforcement learningturn-level credit assignmentrecursive self-distillationcritic-free methodsBayesian belief states