is because simultaneous gradient descent can sometimes behave a little bit like min Max and a little bit like max min and we're just unlucky enough that it often behaves more like max min and does the thing that we don't want some people have explained mode collapse in terms of the fact that we use the reverse KL loss that I described earlier when I said that I don't believe the reverse KL loss it describes why we get sharp samples because the reverse KL loss would prefer to choose a single mode rather than averaged out two different modes it does