arXiv:2606.28242v1 Announce Type: new Abstract: Understanding how performance scales jointly with model size and data is a central problem in modern machine learning. Existing theoretical works on scaling laws typically describe generalization as a function of data or compute, often in fixed-feature or infinite-width regimes and for online SGD. Here, we instead study how generalization scales with the number of trainable parameters and the number of samples in a feature-learning model. We analyze $\ell_2$-regularized empirical test error minimization in a quadratic two-layer network in a finit
This research provides new theoretical insights into AI scaling laws, a critical area given the rapid advancements and increasing resource demands of large models.
Understanding how AI performance scales with data and model size is fundamental for strategic planning in AI development, resource allocation, and competitive advantage.
This research contributes to a more nuanced theoretical understanding of AI generalization, moving beyond fixed-feature or infinite-width assumptions.
- · AI researchers
- · Hyperscalers
- · Organizations developing large AI models
- · AI development relying solely on empirical trial-and-error
Improved theoretical models for predicting AI model performance and resource requirements.
More efficient allocation of compute and data resources in training large AI models, potentially reducing development costs.
Accelerated development of more powerful and generalizable AI systems, further compressing innovation cycles.
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