duddal:

If the model indeed respects the other losses than txty_loss, why though?


I don’t know, but maybe the losses try to “pull” the model parameters into different directions?
E.g. what would happen if the 4 losses expect positive output values of the model to reduce their loss while txty_loss expects negative outputs? This is of course a very simplified view of this use case, but could something similar happen in your training?