text: This paper proposes the use of optimistic mirror descent to train Wasserstein Generative Adversarial Networks (WGANS).  The authors remark that the current training of GANs, which amounts to solving a zero-sum game between a generator and discriminator, is often unstable,  and they argue that one source of instability is due to limit cycles, which can occur for FTRL-based algorithms even in convex-concave zero-sum games.  Motivated by recent results that use Optimistic Mirror Descent  (OMD) to achieve faster convergence rates (than standard gradient descent) in convex-concave zero-sum games and normal form games, they suggest using these techniques for WGAN training as well.  The authors prove that, using OMD, the last iterate converges to an equilibrium and use this as motivation that OMD methods should be more stable for WGAN training.  They then compare OMD against GD on both toy simulations and a DNA sequence task before finally introducing an adaptive generalization of OMD, Optimistic Adam, that they test on CIFAR10.  This paper is relatively well-written and clear,  and the authors do a good job of introducing the problem of GAN training instability as well as the OMD algorithm, in particular highlighting its differences with standard gradient descent as well as discussing existing work that has applied it to zero-sum games.  Given the recent work on OMD for zero-sum and normal form games, it is natural to study its effectiveness in training GANs. The issue of last iterate versus average iterate for non convex-concave problems is also presented well.   The theoretical result on last-iterate convergence of OMD for bilinear games is interesting, but somewhat wanting  as it does not provide an explicit convergence rate as in Rakhlin and Sridharan, 2013.  Moreover, the result is only at best a motivation for using OMD in WGAN training  since the WGAN optimization problem is not a bilinear game.  The experimental results seem to indicate that OMD is at least roughly competitive with GD-based methods,  although they seem less compelling than the prior discussion in the paper would suggest.  In particular, they are matched by SGD with momentum when evaluated by last epoch performance  (albeit while being less sensitive to learning rates).  OMD does seem to outperform SGD-based methods when using the lowest discriminator loss,  but there doesn't seem to be even an attempt at explaining this in the paper.  I found it a bit odd that Adam was not used as a point of comparison in Section 5, that optimistic Adam was only introduced and tested for CIFAR but not for the DNA sequence problem,  and that the discriminator was trained for 5 iterations in Section 5 but only once in Section 6,  despite the fact that the reasoning provided in Section 6 seems like it would have also applied for Section 5.  This gives the impression that the experimental results might have been at least slightly "gamed".  For the reasons above, I give the paper high marks on clarity, and slightly above average marks on originality, significance, and quality. Specific comments:Page 1, "no-regret dynamics in zero-sum games can very often lead to limit cycles":  I don't think limit cycles are actually ever formally defined in the entire paper.   Page 3, "standard results in game theory and no-regret learning":  These results should be either proven or cited. Page 3: Don't the parameter spaces need to be bounded for these convergence results to hold?  Page 4, "it is well known that GD is equivalent to the Follow-the-Regularized-Leader algorithm":  For completeness, this should probably either be (quickly) proven or a reference should be provided. Page 5, "the unique equilibrium of the above game is...for the discriminator to choose w=0":  Why is w=0 necessary here? Page 6, "We remark that the set of equilibrium solutions of this minimax problem are pairs (x,y) such that x is in the null space of A^T and y is in the null space of A":  Why is this true?  This should either be proven or cited. Page 6, Initialization and Theorem 1: It would be good to discuss the necessity of this particular choice of initialization for the theoretical result.  In the Initialization section, it appears simply to be out of convenience. Page 6, Theorem 1: It should be explicitly stated that this result doesn't provide a convergence rate, in contrast to the existing OMD results cited in the paper.    Page 7, "we considered momentum, Nesterov momentum and AdaGrad":  Why isn't Adam used in this section if it is used in  later experiments? Page 7-8, "When evaluated by....the lowest discriminator loss on the validation set, WGAN trained with Stochastic OMD (SOMD) achieved significantly lower KL divergence than the competing SGD variants.":  Can you explain why SOMD outperforms the other methods when using the lowest discriminator loss on the validation set?  None of the theoretical arguments presented earlier in the paper seem to even hint at this.  The only result that one might expect from the earlier discussion and results is that SOMD would outperform the other methods when evaluating by the last epoch.  However, this doesn't even really hold,  since there exist learning rates in which SGD with momentum matches the performance of SOMD. Page 8, "Evaluated by the last epoch, SOMD is much less sensitive to the choice of learning rate than the SGD variants":  Learning rate sensitivity doesn't seem to be touched upon in the earlier discussion.  Can these results be explained by theory? Page 8, "we see that optimistic Adam achieves high numbers of inception scores after very few epochs of training":  These results don't mean much without error bars. Page 8, "we only trained the discriminator once after one iteration of generator training. The latter is inline with the intuition behind the use of optimism....":  Why didn't this logic apply to the previous section on DNA sequences, where the discriminator was trained multiple times?