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English(EN) A Quadratic Lens on Muon's Polar Update: Benefits, Pitfalls, and the Role of Newton-Schulz

研究论文分析 Muon 优化器的 PolarGD 更新方法

一篇新发表在 arXiv 上的研究论文分析了 Muon 优化器的核心组件“PolarGD”更新方法。该研究使用矩阵二次型进行分析,表明当梯度远离低秩且在其最大奇异方向上表现出高曲率时,PolarGD 的正交化策略具有优势。论文还指出,在特定场景下,PolarGD 的表现优于标准梯度下降和符号下降,可作为一种预条件梯度下降的变体。 AI

影响 为可能导致大型语言模型更快训练的优化技术提供了理论见解。

排序理由 该集群包含一篇学术论文,详细介绍了对机器学习中使用的优化方法的新分析。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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研究论文分析 Muon 优化器的 PolarGD 更新方法

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该集群包含一篇学术论文,详细介绍了对机器学习中使用的优化方法的新分析。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Egor Shulgin, Sam Laing, Antonio Orvieto, Peter Richt\'arik ·

    A Quadratic Lens on Muon's Polar Update: Benefits, Pitfalls, and the Role of Newton-Schulz

    arXiv:2610.30280v1 Announce Type: new Abstract: Muon updates matrix-valued parameters along approximately orthogonalized directions and often trains large language models faster than AdamW, yet it remains unclear when and why orthogonalization helps. We study PolarGD, the orthogo…