I see, so the implicit assumption of minimum loss being zero
enforces the minimization with positive loss and maximization with negative loss.
I am currently experimenting something and my custom loss function can be less than zero.
Based on what you told me, the framework will maximize the loss to zero, when loss is negative and minimize the loss to zero, when loss is positive.
I.e. Loss should be going up and down around 0 instead of reaching a single convergence point
Is my understanding correct?