AMUSE is a new optimizer integrating Muon orthogonalization with Schedule-Free averaging via adaptive interpolation for schedule-free anytime training that improves Pareto frontiers on vision and LLM tasks.
Connections between schedule-free optimizers, AdEMAMix, and accelerated sgd variants.arXiv preprint arXiv:2502.02431
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SODA unifies several modern optimizers under optimistic dual averaging and supplies a 1/k decay wrapper that improves performance without weight decay tuning.
Muon optimizer outperforms AdamW across 17 tabular datasets when training MLPs under a shared protocol.
ScheduleFree+ scales schedule-free learning to LLMs with fixes for large batches and models, outperforming Warmup-Stable-Decay schedules by up to 31% at 1000 tokens per parameter.
citing papers explorer
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AMUSE: Anytime Muon with Stable Gradient Evaluation
AMUSE is a new optimizer integrating Muon orthogonalization with Schedule-Free averaging via adaptive interpolation for schedule-free anytime training that improves Pareto frontiers on vision and LLM tasks.
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Optimistic Dual Averaging Unifies Modern Optimizers
SODA unifies several modern optimizers under optimistic dual averaging and supplies a 1/k decay wrapper that improves performance without weight decay tuning.
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Benchmarking Optimizers for MLPs in Tabular Deep Learning
Muon optimizer outperforms AdamW across 17 tabular datasets when training MLPs under a shared protocol.
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ScheduleFree+: Scaling Learning-Rate-Free & Schedule-Free Learning to Large Language Models
ScheduleFree+ scales schedule-free learning to LLMs with fixes for large batches and models, outperforming Warmup-Stable-Decay schedules by up to 31% at 1000 tokens per parameter.