Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why

On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this signal is beneficial and under which it is detrimental. Which teacher model should be used, and in the case of self-distillation, which specific context should serve as the supervisory signal? Does the optimal choice vary from one token to the next? At present, addressing these questions typically requires costly training runs whose aggregate performan

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ethics.ai (11 May 2026), “Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why,” evidence record 4538, https://ethics.ai/record/4538 (originally published by arXiv).

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