Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation
Self-distillation (SD) has emerged as a compute-efficient alternative to reinforcement learning with verifiable rewards: a self-teacher, conditioned on privileged information (PI) about the answer such as a reference solution, supplies dense per-token supervision to a student that never sees it. Reported gains, however, come almost exclusively from narrow, low-difficulty settings, leaving open a basic question: as a lone objective, with no reward term, does SD teach anything? We reproduce SDPO's
Record details
Published: 5 August 2026
Source: arXiv cs.AI
Category: Research
Topics: Bias & fairness · Children & education
Retrieved: 6 August 2026
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How to cite this record
ethics.ai (5 August 2026), “Privileged, but Biased: How PI-Conditioned Teachers Break Self-Distillation,” evidence record 16934, https://ethics.ai/record/16934 (originally published by arXiv cs.AI).
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