Hey, I’m a little new to pytorch and have been playing around. I noticed that after computing the loss, I am unable to affect the value of loss whatsoever. For eg, if say the loss value is 10, if I do (loss * 1.5).backward() or any number for that matter, it doesn’t seem to affect the weights any differently than when the loss is just 10. I did this with a very small network and dummy data and printed out the outputs from copies of network after backpropping and stepping different losses ( loss.backward() for the original and (loss*1.5).backward() for the copy ) and both of the outputs came out the same. While I did come across a post that mentioned something similar to this where it says the gradient along with the sign is what matters, I don’t understand how I would handle cases where I have a coefficient for loss as in many reinforcement learning algorithms. How can I successfully use a coefficient for the loss when I call backward? Or does (loss * 1.5).backward() actually work and is my understanding is wrong?