Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation

Large language model agents are commonly trained through reinforcement learning with sparse trajectory-level rewards, which offer limited guidance on how strongly individual tokens should be updated. On-Policy Self-Distillation (OPSD) addresses this by re-scoring generated tokens under a privileged replay view to obtain dense, token-level supervision. However, we identify a confounding issue: the resulting support may reflect both the privileged information contained in the replay view and score

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ethics.ai (5 August 2026), “Agentic Reinforcement Learning with Observation-Calibrated Self-Distillation,” evidence record 16656, https://ethics.ai/record/16656 (originally published by arXiv).

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