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Evidence record 18385 · automatically gathered

On-Policy Self-Distillation without Any Supervision

On-policy (Self-)Distillation (OPD / OPSD) has shown strong potential for post-training large language models (LLMs). However, existing methods still rely heavily on external supervision, including ground-truth signals, environmental feedback, or guidance from larger models, and therefore fall short of genuine "self"-distillation. In this study, we show that on-policy self-distillation can be achieved using only a model's own generations via internal consistency. We propose unsupervised on-polic

Record details

Published: 8 August 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Regulation · Environment
Retrieved: 12 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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Shared topic: Regulation · Shared topic: Environment

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Shared topic: Regulation · Shared topic: Environment

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HuggingFace Daily Papers · 5 August 2026

How to cite this record

ethics.ai (8 August 2026), “On-Policy Self-Distillation without Any Supervision,” evidence record 18385, https://ethics.ai/record/18385 (originally published by HuggingFace Daily Papers).

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