Of note is the lack of a clear trend towards higher scoring molecules with increasing reward learning contribution as reported in . Instead the low scoring modes all lie in the low reward learning/high adversarial learning regime (the right axis of the ternary plots in Figure ). We attribute this to two factors. First, GAN convergence occurs on a much longer time scale than learning by MLE and reward optimization i.e., the discriminator has not yet learned to distinguish between real and fake molecules well enough to for adversarial training to have substantial (more so than our other objectives) effect on generator performance. Second, this suggests that there is a similarity to likelihood learning and reward optimization. This is not altogether surprising since normalizing flows are trained to generate maximally probable outputs which is an implicit form of our reward learning paradigm.