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This paper studies how much orthogonalization the Muon optimizer requires, proposing a five-step cubic Newton-Schulz schedule that reduces computational cost while achieving training quality similar to more expensive methods across GPT-2 Small and hybrid MoE/Mamba models.
Discussion of a finding that all softmax/linear attention variants can be interpolated, and that the Muon optimizer is crucial for Parallax to move beyond Softmax Attention. Includes link to paper and code.
SignMuon is a 1-bit, matrix-aware optimizer for distributed training that combines signSGD's majority-vote sign aggregation with Muon's polar-step framework, achieving 32x bandwidth reduction over float32 while maintaining strong convergence and performance on benchmarks like CIFAR-10/ResNet-50 and nanoGPT.