**Abstract:** We study the effect of gradient-based optimization on feature learning in two-layer neural networks. We consider a setting where the number of samples is of the same order as the input dimension and show that, when the input data is isotropic, gradient descent always improves upon the initial random features model in terms of prediction risk, for a certain class of targets. Further leveraging the practical observation that data often contains additional structure, i.e., the input c…