Fabien Roger and I demonstrated that you can embed arbitrary state machines into the backward pass of neural nets built from standard components, allowing them to “remember” arbitrary facts about their training distribution and modulate their behavior based on this. The construction is pretty artificial, but I think that it is probably a useful starting point for people who want to more carefully understand the potential for gradient hacking.
Fabien Roger and I demonstrated that you can embed arbitrary state machines into the backward pass of neural nets built from standard components, allowing them to “remember” arbitrary facts about their training distribution and modulate their behavior based on this. The construction is pretty artificial, but I think that it is probably a useful starting point for people who want to more carefully understand the potential for gradient hacking.