Gradient hacking is usually discussed in the context of deceptive alignment. This is probably where it has the largest relevance to AI safety but if we want to better understand gradient hacking, it could be useful to take a broader perspective and study it on its own (even if in the end, we only care about gradient hacking because of its inner alignment implications).
In the most general setting, gradient hacking could be seen as a way for the agent to “edit its source code”, though probably only in a very limited way. I think it’s an interesting question which kinds of edits are possible with gradient hacking, for example whether an agent could improve its capabilities this way.
Gradient hacking is usually discussed in the context of deceptive alignment. This is probably where it has the largest relevance to AI safety but if we want to better understand gradient hacking, it could be useful to take a broader perspective and study it on its own (even if in the end, we only care about gradient hacking because of its inner alignment implications). In the most general setting, gradient hacking could be seen as a way for the agent to “edit its source code”, though probably only in a very limited way. I think it’s an interesting question which kinds of edits are possible with gradient hacking, for example whether an agent could improve its capabilities this way.