AMO adapts Newton-Schulz iteration counts per operator type using early-training geometry measurements to improve Muon optimizer performance over uniform schedules in LLM pre-training.
Cosmos: A hybrid adaptive optimizer for memory-efficient training of llms
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AMO: Adaptive Muon Orthogonalization
AMO adapts Newton-Schulz iteration counts per operator type using early-training geometry measurements to improve Muon optimizer performance over uniform schedules in LLM pre-training.