arXiv:2603.14315v2 Announce Type: replace Abstract: While spectral-based optimizers like Muon operate directly on the spectrum of updates, standard adaptive methods such as AdamW do not account for the spectral structure of weights and gradients, leaving them vulnerable to two empirical issues in large language model (LLM) training: (i) the optimizer updates can have large spectral norms, potentially destabilizing training and degrading generalization; (ii) stochastic gradient noise can exhibit sparse spectral spikes, with a few dominant singular values much larger than the rest. We propose SP
The continuous scaling of large language models necessitates more stable and efficient training methods to overcome existing empirical challenges, making advances in optimization critical.
Improved LLM training stability and generalization directly impact the feasibility and cost of developing next-generation AI, benefitting all sectors reliant on advanced AI capabilities.
This research introduces a new spectral-based optimization approach that addresses long-standing issues with standard adaptive methods, potentially making LLM training more robust and performant.
- · AI research institutions
- · Hyperscalers
- · LLM developers
- · AI-powered software companies
- · Inefficient LLM training pipelines
More stable and efficient training of large language models.
Faster development and deployment of more capable and reliable AI systems across industries.
Reduced computational costs for AI development, potentially democratizing access to powerful AI models.
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Read at arXiv cs.LG