MuonGS relaxes weight-norm control for LLM pretraining
On dense models it reports about 15% token-equivalent speedup versus MuonH at matched validation loss.
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On dense models it reports about 15% token-equivalent speedup versus MuonH at matched validation loss.
Weight decay causally regulates massive-activation scale in transformers, with peak magnitude scaling near lambda to the -1/2.
Paper derives a spectral theory showing weight decay drives NTK feature learning that controls grokking timescale and phase structure.
Researchers derive an exact law linking learning-rate schedules and weight decay in normalized networks, pinpointing when scale-invariant optimization destabilizes.
Theoretical framework bounds generalization for gradient descent with weight decay, deriving conditions that explain delayed generalization and grokking.
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