Optimization and Generalization in ML — Ch.06: Scaling Laws and Modern Training [EN]

Scaling laws in machine learning offer a systematic approach to understanding how performance changes as models, datasets, and computational resources grow. These laws reveal relationships between resource allocation and predictive performance, helping researchers anticipate benefits and limitations of larger learning systems. The importance of scaling laws has grown as deep learning models become more expensive to train, making traditional trial-and-error experimentation less practical.

By connecting model development decisions with expected improvements in learning performance, scaling laws provide a quantitative perspective on resource allocation in modern machine learning systems.

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