loop and that very elegantly gets us around the mode collapse problem another really big important research direction for generative address that works is figuring out how to evaluate them this is actually a problem that's broader than just generative address donuts it's a problem for generative models across the board models with good likelihood can produce bad samples models with good samples can actually have a very bad likelihood and then even when we talk about good samples and bad samples there's not really a very effective way