Review for NeurIPS paper: Directional convergence and alignment in deep learning

Weaknesses: I have two main critiques on this work. The first relates to the significance of its results. In the setting studied, directional convergence, alignment and margin maximization have all been treated in several recent works (which the paper refers to). I know that at least in some of these works directional convergence and/or alignments were assumed (not proven), but nonetheless, my feeling is that the paper does not draw a sufficiently clear line separating itself from existing literature. For example, a very relevant existing work --- Lyu and Li 2019 --- is said to have left open the issues of directional convergence and alignment, but to my knowledge, that work does establish directional convergence, at least in some settings.