In the example you stated, after applying you get the error 0.7. And when you do a backward pass the network computes the gradients such that your error is reduced. I am not sure how you stated the gradients will increase in your example.
Also like smth said you don’t need to do backward twice, you can basically add those losses and directly compute backward on the total loss.