English(EN) Scalable Kronecker-Fisher Approximation: Efficient Hessian Analysis for Billion-Parameter Language Models Compression

新的 Hessian 分析方法有助于压缩十亿参数模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-03 04:00

研究人员开发了一种新颖的可扩展 Kronecker-基近似方法，用于分析十亿参数语言模型的 Hessian。该方法能够有效地捕捉跨层交互，而无需存储完整的 Fisher 矩阵，并揭示了值投影层始终是最敏感的组件。该近似方法与性能下降和恢复情况高度相关，为大型模型的引导式压缩和优化策略提供了实用的工具。 AI

影响为识别大型模型中的脆弱组件提供了实用工具，从而能够实现引导式压缩和优化策略。

排序理由学术论文，详细介绍了一种分析大型语言模型的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Viacheslav Yusupov, Daria Cherniuk, Evgeny Frolov · 2026-09-03 04:00

可扩展的 Kronecker-Fisher 近似：用于十亿参数语言模型压缩的高效 Hessian 分析

arXiv:2609.02451v1 Announce Type: cross Abstract: In this paper, we propose a scalable Kronecker-based approximation that captures cross-layer interactions without storing the entire Fisher matrix, enabling practical Hessian analysis for billion-parameter networks where full comp…

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可扩展的 Kronecker-Fisher 近似：用于十亿参数语言模型压缩的高效 Hessian 分析

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