Thank you Frank.
Oh I thought, loss affects the gradient descent directly.
So it simply provides the search space and training a model purely depends on learning rate, not the loss
flipping the sign of loss turns the problem into maximization because within the search space everything is flipped (min point becoming max point).
Is this correct?