On power-law covariance least squares problems, SignSVD (Muon) and SignSGD (Adam proxy) show three phases of relative performance depending on data exponent α and target exponent β.
Dimension-adapted momentum outscales sgd
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Muon achieves higher storage capacity than SGD and matches Newton's method in one-step recovery rates for associative memory under power-law distributions, while saturating at larger critical batch sizes and showing faster initial multi-step dynamics.
In a random feature model, optimal SGD learning-rate schedules are polynomial decay in the easy phase and warmup-stable-decay in the hard phase, outperforming constant or simple power-law schedules and transferring differently across training horizons.
Lower bounds establish that heavy-ball momentum extends the compute-efficient batch-size window by sqrt(kappa) over SGD in linear regression, with accelerated SGD showing spectrum-dependent CE-serial runtime tradeoffs.
citing papers explorer
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Phases of Muon: When Muon Eclipses SignSGD
On power-law covariance least squares problems, SignSVD (Muon) and SignSGD (Adam proxy) show three phases of relative performance depending on data exponent α and target exponent β.
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Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory
Muon achieves higher storage capacity than SGD and matches Newton's method in one-step recovery rates for associative memory under power-law distributions, while saturating at larger critical batch sizes and showing faster initial multi-step dynamics.
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Theory of Optimal Learning Rate Schedules and Scaling Laws for a Random Feature Model
In a random feature model, optimal SGD learning-rate schedules are polynomial decay in the easy phase and warmup-stable-decay in the hard phase, outperforming constant or simple power-law schedules and transferring differently across training horizons.
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Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods
Lower bounds establish that heavy-ball momentum extends the compute-efficient batch-size window by sqrt(kappa) over SGD in linear regression, with accelerated SGD showing spectrum-dependent CE-serial runtime tradeoffs.