Muon优化器新突破，基于重尾谱校正解决噪声方向过量问题，助力大模型高效训练
arXiv:2603.10067v2 Announce Type: replace-cross Abstract: Muon has recently shown promising results in LLM training. In this work, we study how to fur…
提出极简优化器设计，大幅降低大模型预训练内存占用，已被ICML 2026接收。
arXiv:2506.16659v3 Announce Type: replace-cross Abstract: Training large language models (LLMs) relies on adaptive optimizers such as Adam, which intr…
深入解读从SGD到Muon的优化器演进，以Schatten-p范数统一矩阵几何约束，为AI研究者提供理论新视角
arXiv:2605.19781v1 Announce Type: new Abstract: Modern optimizers, like Muon, impose matrix-wise geometry constraints on their updates. These matrix-w…
揭秘SGD在LLM预训练中不如Adam的根源：大有效学习率的关键作用。
arXiv:2605.17787v1 Announce Type: new Abstract: It is widely believed that stochastic gradient descent (SGD) performs significantly worse than adaptiv…
Muon优化器在视觉语言对齐与强化学习微调中暴露频谱失效问题，作者提出高通滤波器补救方案，刷新大模型训练认知。
arXiv:2605.19282v1 Announce Type: new Abstract: Muon is a matrix-aware optimizer that leverages Newton-Schulz (NS) iterations to enforce spectral grad…
这篇论文提出了DynMuon，从动态谱成形视角重新理解Muon优化器，为训练大模型提供理论洞察与方法改进。
arXiv:2605.17109v1 Announce Type: new Abstract: In recent years, Muon has emerged as the dominant method for training large language models, and trans…