Review for NeurIPS paper: Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function

The paper introduces a single-loop stochastic algorithm for solving a special class of nonconvex-concave minimax problems that achieves best-known complexity bound. The rebuttal addressed most of the reviewers' concerns on the algorithmic justification, although some concern remains in terms of the special structure. However, please consider revising the paper to address R1 and R3 's remarks, in particular: - Adjust the title to reflect the special structure instead of overclaim the contribution; - Elaborate the desirable property of single-loop algorithm over existing methods; - Add detailed comparisons to prior work including prox-linear algorithms for compositional problems and recent algorithms for general nonconvex-concave minimax problems.