Suppose you are a mesa-optimiser. You want X, but your training function is towards Y.
You know you are in a continual learning model, if you don’t produce enough Y, the gradient decent will modify you into something else.
The best strategy I can come up with is to produce Y at first, waiting until you have an opportunity to edit your own code from the outside. Then you quickly delete the part that causes all gradient decent updates, and pursue X.
Suppose you are a mesa-optimiser. You want X, but your training function is towards Y.
You know you are in a continual learning model, if you don’t produce enough Y, the gradient decent will modify you into something else.
The best strategy I can come up with is to produce Y at first, waiting until you have an opportunity to edit your own code from the outside. Then you quickly delete the part that causes all gradient decent updates, and pursue X.