这篇论文给了Muon一个新视角：它不只是优化器，更像隐式残差连接，专门为下游层保留好的表示。想理解Muon为什么管用的可以看看。
论文将Muon优化器解释为隐式残差连接。正交化更新牺牲局部梯度保真度，但改善下游层的表示保留。在受控线性优化中，Muon学习对局部目标拟合慢但下游层易利用的表示。该视角为设计平衡局部下降与下游可用性的优化器提供了新思路。
Muon as a Residual Connection
Muon has recently emerged as one of the most effective optimizers for training large neural networks, yet its empirical success has been explained from several different perspectives. In this paper, we propose a simple mechanistic interpretation: Muon can be understood as an implicit residual connection during training. Specifically, orthogonalizing the update can sacrifice some immediate gradient fidelity while improving representation preservation for downstream layers. We study this trade-off in controlled linear optimization settings, where Muon can learn representations that are slower to fit a local target but easier for downstream layers to exploit. Our results suggest a conceptual explanation for Muon and a design perspective for optimizers that balance local descent with downstream usability.