English(EN) GradMAP: Faster Layer Pruning with Gradient Metric and Projection Compensation

新的 GradMAP 方法将 LLM 层剪枝速度提高了 4 倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-07-31 04:00

研究人员开发了 GradMAP，一种用于剪枝大型语言模型 (LLM) 层以降低计算成本的新颖方法。该技术利用单次反向传播的梯度幅度来有效评估层的重要性，并采用投影补偿矩阵来减轻性能下降。实验表明，GradMAP 在剪枝速度上比现有方法提高了四倍，同时保持了卓越的性能。 AI

影响该方法可以显著降低部署大型语言模型的计算要求，使其在更广泛的应用中更易于访问和实用。

排序理由该集群包含一篇详细介绍 LLM 层剪枝新方法的 ist 研究论文。

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arXiv cs.CL TIER_1 English(EN) · Hao Liu, Guangyan Li, Wensheng Zhang, Yongqiang Tang · 2026-07-31 04:00

GradMAP：通过梯度度量和投影补偿实现更快的层剪枝

arXiv:2602.14649v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning abilities, but their high computational costs limit their practical deployment. Recent studies reveal significant redundancy in LLMs layers, making layer pruning an active re…