two losses = two objectives = two probability distributions being modeled = two “tasks”
It is usually fine if network capacity is big enough, tasks are related (“shared” feature space representations are possible) and value magnitudes are similar.



 Kirito000:

The gradient norm increases. I’m not sure about the effect and the underlying meaning of gradient norm.


Your network is not yet trained for the introduced objective, so gradients increase.