How do neural scaling laws emerge from cost function dynamics?

Scaling laws emerge because cost reduction follows predictable power-law behavior across model size and data.

Empirical studies show that loss decreases as a smooth power law function of compute, dataset size, and model parameters. This arises from statistical efficiency limits of learning and entropy reduction in data compression. As model capacity increases, the cost function becomes easier to optimize up to a point where data becomes the bottleneck.