superficially seem like it might explain why we get mode collapse but I don't think that it is actually the explanation in this case for one thing if we use the forward KL we still get mode collapse in many cases also the reverse KL divergence does not say that we should collapse to a single mode it says that if our model is not able to represent every mode and to put sharp divisions between them then it should discard modes rather than blur modes but it would still prefer to have as many modes as the model can represent and