arXiv:2605.21803v1 Announce Type: new Abstract: Scaling laws have made language-model performance predictable from model size, data, and compute, but they typically treat the optimizer as a fixed training detail. We show that this assumption misses a fundamental axis of representation scaling: how effectively the optimizer converts added FFN width into utilized spectral capacity. Using eigenspectra of feed-forward network representations, measured through soft and hard spectral-ranks, we find that \emph{the same Transformer architecture realizes markedly different spectral scaling laws when tr
The proliferation of increasingly complex AI models and the pursuit of optimal performance are driving deeper research into the foundational mechanisms of AI training and scaling.
Understanding how optimizers influence the effective capacity of AI models provides a critical lever for improving efficiency and performance, potentially altering the resource requirements for advanced AI.
The assumption that optimizers are merely a fixed training detail is challenged, revealing them as fundamental drivers of architectural capacity and scaling laws, impacting how models are designed and scaled.
- · AI researchers and developers
- · Cloud providers focusing on AI infrastructure
- · Companies investing in foundation model development
- · Developers relying solely on brute-force scaling without optimization insights
- · Hardware manufacturers if efficiency gains reduce compute demand per unit of per
Refined understanding of AI model scaling will lead to more efficient and powerful large language models.
New architectural design principles and training methodologies will emerge to maximize optimizer-induced capacity.
Reduced compute requirements for achieving high-performance AI could democratize access to advanced AI capabilities.
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Read at arXiv cs.LG