SignSGD provably beats SGD by a factor of d under sparse noise via matched ℓ1-norm upper and lower bounds, with an equivalent result for Muon on matrices, and this predicts faster GPT-2 pretraining.
Sign Operator for Coping with Heavy-Tailed Noise: High Probability Convergence Bounds with Exten- sions to Distributed Optimization and Comparison Oracle
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Decentralized SGD achieves high-probability convergence with order-optimal rates and linear speedup under standard cost assumptions matching those for MSE convergence.
LionMuon alternates Lion and Muon steps with shared dual-EMA buffer to Pareto-dominate existing optimizers in loss and compute on models up to 720M parameters.
SoftSignum replaces hard sign with soft-sign in optimizers via temperature control and quantile scheduling, extends to SoftMuon, provides a convergence proof for stochastic non-convex settings, and reports better performance than sign-based methods and AdamW on deep learning tasks.
Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.
citing papers explorer
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When and Why SignSGD Outperforms SGD: A Theoretical Study Based on $\ell_1$-norm Lower Bounds
SignSGD provably beats SGD by a factor of d under sparse noise via matched ℓ1-norm upper and lower bounds, with an equivalent result for Muon on matrices, and this predicts faster GPT-2 pretraining.
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High-Probability Convergence Guarantees of Decentralized SGD
Decentralized SGD achieves high-probability convergence with order-optimal rates and linear speedup under standard cost assumptions matching those for MSE convergence.
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LionMuon: Alternating Spectral and Sign Descent for Efficient Training
LionMuon alternates Lion and Muon steps with shared dual-EMA buffer to Pareto-dominate existing optimizers in loss and compute on models up to 720M parameters.
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Softsign: Smooth Sign in Your Optimizer For Better Parameter Heterogeneity Handling
SoftSignum replaces hard sign with soft-sign in optimizers via temperature control and quantile scheduling, extends to SoftMuon, provides a convergence proof for stochastic non-convex settings, and reports better performance than sign-based methods and AdamW on deep learning tasks.
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Constrained Stochastic Spectral Preconditioning Converges for Nonconvex Objectives
Proximal stochastic spectral preconditioning converges for nonconvex constrained objectives under heavy-tailed noise, with a variance-reduced version achieving faster rates and a refined analysis of Muon iterations.