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English(EN) Musec: MomentUm SpEctral Clipping for Stable Muon-type Training

新的Musec优化器增强了LLM训练稳定性

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出了一种新颖的优化器MomentUm SpEctral Clipping (Musec)，旨在稳定大型语言模型的训练。Musec解决了μ子优化器固有的不稳定性问题，该优化器通常优于Adam和AdamW，但可能出现损失尖峰和权重无界增长。与需要架构修改的先前方法不同，Musec实现了一种优化器级别的谱裁剪机制，该机制保留了动量矩阵的谱结构。该论文还详细介绍了一种名为Soft Musec的高效实现，它为非凸、非光滑随机优化提供了理论收敛保证，并在各种学习率和模型大小上展示了经验稳定性改进。 AI

影响 引入了一种更稳定、更高效的大型语言模型训练方法，有望提高性能并减少发散问题。

排序理由 该集群包含一篇详细介绍大型语言模型训练新优化方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的Musec优化器增强了LLM训练稳定性

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该集群包含一篇详细介绍大型语言模型训练新优化方法的论文。[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) · Zhuanghua Liu, Menglian Wang, Luo Luo ·

    Musec：用于稳定μ子类型训练的MomentUm谱截断

    arXiv:2609.11655v1 Announce Type: new Abstract: Muon has emerged as a highly effective optimizer for large language model training, often achieving superior convergence and performance compared with the widely adopted Adam and AdamW optimizers. Nevertheless, Muon is prone to trai…

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