Learning from Consensus and Disagreement: Unsupervised On-Policy Self-Distillation with Minority-Trajectory Contrast
On-policy self-distillation improves language-model reasoning by querying a teacher on states actually visited by the student. Recent methods create a powerful information asymmetry by exposing the teacher to privileged context, yet they fundamentally rely on external supervision---such as gold solutions or verifiers---to construct this advantage. We introduce CoDA (Consensus and Disagreement Alignment), a fully unsupervised framework that creates reliable privileged information entirely from th
Record details
Published: 9 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Regulation · Safety & alignment · Children & education
Retrieved: 11 August 2026
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How to cite this record
ethics.ai (9 August 2026), “Learning from Consensus and Disagreement: Unsupervised On-Policy Self-Distillation with Minority-Trajectory Contrast,” evidence record 18327, https://ethics.ai/record/18327 (originally published by arXiv cs.LG).
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