policy gradients on this reward signal because this isn't like Y prime minus y there's no ground truth with respect to the validation loss of the student achieves so you're just taking that validation loss reward and treating that as like a reward and like say pac-man or Atari and using policy gradients to update the parameters but basically say like if this parameter contributed a lot to the output and then you get a high reward do more of this like increase the weight from this connection to do more of it to get more of this reward that's