arXiv:2509.14562v3 Announce Type: replace Abstract: Large models recently are widely applied in machine learning, so efficient training of large models has received widespread attention. More recently, the useful Muon optimizer is specifically designed for matrix-structured parameters of large models. Although some works have begun to study the Muon optimizer, the existing Muon and its variants still suffer from high sample complexity or high memory for large models. To fill this gap, we propose a light and fast Muon (LiMuon) optimizer for training large models, which builds on the momentum-ba
The proliferation of large models in machine learning necessitates more efficient training methods, driving continuous innovation in optimization algorithms.
Efficient optimization algorithms are critical for reducing the computational and memory costs of training large AI models, impacting accessibility and scalability of advanced AI.
New optimization techniques like LiMuon aim to significantly lower the barriers to entry and operational costs for developing and deploying large AI models.
- · AI developers
- · Cloud providers
- · AI research institutions
- · Hyperscalers
- · Companies with less optimized training infrastructure
- · Developers reliant on older, less efficient optimizers
More researchers and companies can afford to train and experiment with even larger models, accelerating AI development.
Reduced training costs could lead to a proliferation of specialized large models across various industries, creating new market opportunities.
Increased accessibility to large model development might decentralize AI innovation, challenging the dominance of a few major players.
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Read at arXiv cs.LG