Here is my loss structure.
model1 and model2 have same balance tothe total loss.
learnable_params = list(model1.parameters)) + list(model2.parameters))
optimizer = optim.Adam(learnable_params, lr=0.01, betas=(0.9,0.999))
...
optmizer.zero_grad()
...
total_loss = model1_loss + model2_loss
total_loss.backward()
optimizer.step()

If I want to model1 affect to the whole network more than model2’s, multiplying some value to model1_loss works well like this?
total_loss = model1_loss *0.5 + model2_loss

or should I change some gradient ?