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Muon optimizer

topic2 events
papersSEP 11 04:00 UTC

arXiv paper proposes first-principles update geometry for language-model output head

A revised arXiv preprint argues that the spectral norm used by the Muon optimizer is not a good measure of functional change for a language model's output head, because softmax removes scale invariance assumptions. The author proposes deriving an update geometry tailored to how that parameter block actually functions. The work is theoretical and has not been peer reviewed.

optimization/llm-training/arxiv/muon
papersSEP 10 04:00 UTC

Muon-C: Adapting the Muon Optimizer to Convolutional Kernels via Operator Alignment

A new arXiv paper presents Muon-C, a variant of the Muon optimizer designed specifically for convolutional layers. The authors argue that applying Muon through standard matrix unfolding misrepresents the geometry of convolution, since it describes a local patch mapping rather than the convolution operator itself. Their approach instead computes the orthogonalized update direction in a way that aligns with the operator's true structure.

optimizers/deep-learning/convnets/training