#model-size News & Analysis

Scaling Laws, Carefully

Scaling laws represent a foundational empirical principle in deep learning, demonstrating that training loss decreases predictably as model size, dataset size, and compute resources increase following a power-law relationship. This framework is essential for optimizing the allocation of computational resources between model parameters and training data.