PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning

Large language model agents have shown strong potential in complex interactive tasks, yet their reinforcement learning (RL) is often hindered by sparse rewards, as a long multi-turn trajectory may receive only a single outcome-level signal. On-policy self-distillation (OPSD) provides dense token-level supervision from a privileged teacher, but the teacher may not be reliable at every position. Existing methods commonly rely on isolated token-level discrepancies, which can be sensitive to noise,

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ethics.ai (2 August 2026), “PCSD: Persistent Consistency for Self-Distillation in Agentic Reinforcement Learning,” evidence record 16222, https://ethics.ai/record/16222 (originally published by HuggingFace Daily Papers).

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