Summary
DemoPSD is a novel machine learning framework that addresses privileged information leakage in policy distillation through selective adoption of teacher guidance. Proposed by Yunhe Li, Hao Shi, Wenhao Liu, Mengzhe Ruan, Hanxu Hou, Zhongxiang Dai, Shuang Qiu, and Linqi Song, the method steers the student model toward a reverse-KL barycenter target, balancing learning from the teacher with preserving the student's own reasoning capacity. The framework is categorized under cs.LG and cs.AI and is available on arXiv (2607.02502). This approach offers a principled way to mitigate over-reliance on teacher signals in knowledge distillation settings.
Overview
Research area: cs.LG, cs.AI
Authors: Yunhe Li, Hao Shi, Wenhao Liu, Mengzhe Ruan, Hanxu Hou, Zhongxiang Dai, Shuang Qiu, Linqi Song
Published: 2026-07-02
arXiv: 2607.02502
Abstract
We introduce DemoPSD, a novel framework that resolves privileged information leakage through selective adoption of teacher guidance. DemoPSD steers the student toward a reverse-KL barycenter target that balances learning from the teacher with preserving the student's own reasoning capacity.
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