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Relative Kinetic Utility: Calibrating Cross-Layer Credit for Global Structured LLM Pruning

Qian, Tianhao et al.
arXiv (All) · Papers
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computation-and-languagemachine-learning
Global structured pruning requires channels from different layers to compete under a shared sparsity budget, raising two coupled challenges: identifying which channels should be retained and making their scores comparable across layers. Raw channel scores can contain block-common scale that leaves w
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