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

arXiv cs.LG · 2d ago Cached

This paper proposes a method to close the MLP reachability gap in Low-Rank Clone distillation by training the full deployed matrix, resulting in significant improvements in token efficiency and model performance at no additional inference cost.

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