Author

Masato Fujitake

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#artificial intelligence Preprint Oct 2026

EnergyCal: Taming Loss Spikes in Muon-Family Optimizers for Sparse MoE Training

Sparse mixture-of-experts (MoE) language-model training can exhibit large loss spikes with Muon-family optimizers. We introduce EnergyCal, a lightweight rule that attenuates routed-expert updates according to the normalized squared Frobenius norm of their finite-step Newton-Schulz directions. EnergyCal leaves the spect...

Masato Fujitake · 0 citations