PHF: Privileged Hidden Flow for On-Policy Self-Distillation
On-policy self-distillation (OPSD) trains a reasoning model on rollouts sampled from its own policy by matching a privileged teacher that also sees verified reference solutions. Existing OPSD objectives supervise only the output distribution, so privileged context affects training through a token-level divergence without directly supervising the internal computation that produced that distribution. We propose Privileged Hidden Flow (PHF), which additionally distills how a privileged teacher's hi
Record details
Published: 28 June 2026
Source: arXiv
Category: Research
Topics: Regulation
Retrieved: 14 July 2026
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ethics.ai (28 June 2026), “PHF: Privileged Hidden Flow for On-Policy Self-Distillation,” evidence record 473, https://ethics.ai/record/473 (originally published by arXiv).
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