Muon achieves faster convergence and larger stable learning rates by flattening the singular value spectrum of the momentum buffer through orthogonalization, scaling step size with average rather than maximum singular values.
Effective quantization of muon optimizer states
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Pith papers citing it
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cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
MuonEq introduces pre-orthogonalization equilibration schemes that improve Muon optimizer performance during large language model pretraining.
MuonQ achieves stable 4-bit quantization of Muon optimizer states via pre-quantization normalization, singular component decomposition with power iteration, and μ-law companding, matching full-precision loss and accuracy on GPT and LLaMA models with up to 7.3x memory savings.
citing papers explorer
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Spectral Flattening Is All Muon Needs: How Orthogonalization Controls Learning Rate and Convergence
Muon achieves faster convergence and larger stable learning rates by flattening the singular value spectrum of the momentum buffer through orthogonalization, scaling step size with average rather than maximum singular values.
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MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration
MuonEq introduces pre-orthogonalization equilibration schemes that improve Muon optimizer performance during large language model pretraining.
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MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization
MuonQ achieves stable 4-bit quantization of Muon optimizer states via pre-quantization normalization, singular component decomposition with power iteration, and μ-law companding, matching full-precision loss and accuracy on GPT and LLaMA models with up to 7.3x memory savings.