When solving unbalanced minimax optimization problems, single-loop algorithms such as GDA or extragradient method could suffer from inferior convergence.  On the other hand, recent algorithms with best-known complexities require sophisticated multi-loop schemes, which are hard to implement and unrealistic in game applications.  In this talk, we show that for several interesting minimax settings, we can design single-loop algorithms that achieve complexities as good as or even better than state-of-the-art multi-loop algorithms. We discuss how acceleration can be achieved through the idea of primal-dual lifting.