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Zhi-Feng Li

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#machine learning Preprint Sep 2026

Train What You Deploy: Closing the MLP Reachability Gap in Low-Rank Clone Distillation

This work shows that a state-of-the-art weight-inheritance distiller, Low-Rank Clone (LRC), deploys a full-width student MLP but ties training to a teacher-induced slice, leaving 62.5-81.4% of each deployed matrix's independent linear degrees of freedom unreachable-paid for at inference, never trainable.

Wen-Hui Chen, Zhi-Feng Li, Jie Zhou et al. · 0 citations