arXiv:2605.29387v1 Announce Type: new Abstract: The scaling exponent $\alpha$ in neural scaling laws $L(N) \propto N^{-\alpha}$ is commonly treated as a fixed constant set by architecture and data. We present evidence that $\alpha$ depends systematically on the optimizer. In controlled random-feature regression experiments -- the canonical theoretical framework for neural scaling -- we measure $\alpha$ across five optimizer variants and six spectral conditions. Preconditioned optimizers consistently yield steeper scaling (larger $\alpha$), with the $\alpha$-shift increasing across most of the
This research emerges as AI scaling laws become a cornerstone of both academic and industrial AI development, making any variance in these laws highly relevant.
A strategic reader should care because optimizer choice, previously seen as a secondary tuning knob, might fundamentally alter the efficiency and cost-effectiveness of achieving desired model performance.
The understanding of neural scaling laws shifts from architecture and data being the sole determinants to optimizers playing a systematic and significant role, suggesting new avenues for research and engineering.
- · AI researchers focusing on optimization theory
- · Developers of custom AI accelerators
- · Cloud AI providers offering optimized training services
- · Companies with advanced MLOps capabilities
- · AI development relying solely on default optimizers
- · Predictive models of AI progress ignoring optimization
- · Hardware designers blind to optimizer-specific demands
Further research will be directed into co-designing optimizers and architectures to maximize scaling efficiency.
This could lead to a ' Cambrian explosion' of specialized optimizers tailored for specific models or data regimes, driving further performance gains.
The increased efficiency in model training could accelerate the development and deployment of more capable AI models, potentially impacting the compute supply chain and AI agents narratives.
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Read at arXiv cs.LG