tune while fine-tuning while fine-tuning the classification loss yields reasonable downstream performance we find empirically that the joint objective the generative objective and the classification objective works even better okay so even when you fine-tune with this model you have to keep the generative modeling part the generative loss around and then it performs even more better more well whatever that word is so that's also something to think about I think this this paper right here it kind of lays down a lot of cool