min which one are we actually doing the way that we train models we do simultaneous gradient descent on both players costs and that looks very symmetric it doesn't naturally prioritize one direction of the min Max or max min in practice we find that we often see results that look an awful lot like Max min unfortunately with G in the inner loop so using some very nice visualizations from Luke Metz and his collaborator collaborators we see here that if we have a target distribution we'd like to learn with several