Yes I’m aware that many are using terminology this way; that’s why I’m complaining about it :)
I think your two 2018 Victoria Krakovna links (in context) are all consistent with my narrower (I would say “traditional”) definition. For example, the CoastRunners boat is actually getting a high RL reward by spinning in circles. Even for non-RL optimization problems that she mentions (e.g. evolutionary optimization), there is an objective which is actually scoring the result highly. Whereas for an example of o3 deleting a unit test during deployment, what’s the objective on which the model is actually scoring highly?
Getting a good evaluation afterwards? Nope, the person didn’t want cheating!
The literal text that the person said (“please debug the code”)? For one thing, erasing the unit tests does not satisfy the natural-language phrase “debugging the code”. For another thing, what if the person wrote “Please debug the code. Don’t cheat.” in the prompt, and o3 cheats anyway? Can we at least agree that this case should not be called reward hacking or specification gaming? It’s doing the opposite of its specification, right?
As for terminology, hmm, some options include “lying and cheating”, “ruthless consequentialist behavior” (I added “behavior” to avoid implying intentionality), “loophole-finding”, or “generalizing from a training process that incentivized reward-hacking via cheating and loophole-finding”.
(Note that the last one suggests a hypothesis, namely that if the training process had not had opportunities for successful cheating and loophole-finding, then the model would not be doing those things right now. I think that this hypothesis might or might not be true, and thus we really should be calling it out explicitly instead of vaguely insinuating it.)