AINeutralLil'Log (Lilian Weng) · 2d ago7/10
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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.