Groups
Category
Double descent describes how test error first follows the classic U-shape with increasing model complexity, spikes near the interpolation threshold, and then drops again in the highly overparameterized regime.
Scaling laws say that model loss typically follows a power law that improves predictably as you increase parameters, data, or compute.