SCALING LAWS, CAREFULLY

Summary from the TLDR AI newsletter

Scaling laws are one of the most critical empirical findings in deep learning. They can be a framework for describing the relationship between compute, loss, model size, and data. Their predictability makes them highly valuable in practice. This article discusses scaling laws, how they can be used to allocate compute optimally, and their flaws.