[bloep]

What I had in mind was kind of like a reward model that is trained by on longer outputs that have a very high similarity to training examples. Something similar has been done to prevent LLMs from using toxic language. You'd simply backprop through that model like in GANs. And no it does not contradict the overall training objective completely because the criterion would be long verbatim copies and it would not affect shorter copies of sound fragments and the like which you would want a music model to produce in order for it to sound realistic and natural.

[p1esk]

Oh OK, so you mean training the model after it has already been trained on the main task, right? Like finetuning. Yes, I think the GAN-like finetuning is a good idea. Though it's less clear where the labels would come from, it seems like some sort of fingerprint would need to be computed for each generated sequence, and this fingerprint would need to be compared against a database of fingerprints for every sequence in the training set. This could be a huge database.

[bloep]

You'd need something Spotify.

Another similar possibility might be to do more RL with this data, e.g. using upside-down RL. One can possibly steer this with user feedback as well.