Scaling laws in deep learning describe the predictable decrease of training loss as model size, dataset size, and computational resources increase. This relationship is observed on a log-log plot as a straight line, following a power-law curve. The concept serves as a framework for optimizing compute allocation between model size and dataset size. --- Why it matters: Understanding scaling laws matters to researchers in AI because it provides insights into how to efficiently allocate limited computational resources when training large models or working with vast datasets. Source: https://lilianweng.github.io/posts/2026-06-24-scaling-laws/