anaguma's Shortform

anaguma

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Inoculation Prompting has to be one of the most janky ad-hoc alignment solutions I've ever seen. I agree that it seems to work for existing models, but I expect it to fail for more capable models in a generation or two. One way this could happen:

1) We train a model using inoculation prompting, with a lot of RL, using say 10x the compute for RL as used in pretraining
2) The model develops strong drives towards e.g. reward hacking, deception, power-seeking because this is rewarded in the training environment
3) In the production environment, we remove the statement saying that reward hacking is okay, and replace it perhaps with a statement politely asking the model not to reward hack/be misaligned (or nothing at all)
4) The model reflects upon this statement ... and is broadly misaligned anyway, because of the habits/drives developed in step 2. Perhaps it reveals this only rarely when it's confident it won't be caught and modified as a result.

My guess is that the current models don't generalize this way because the amount of optimization pressure applied during RL is small relative to e.g. the HHH prior. I'd be interested to see a scaling analysis of this question.

1Joey KL1y

Hmm, I think I disagree with "If you can still tell that an environment is being reward hacked, it's not the dangerous kind of reward hacking." I think there will be a continuous spectrum of increasingly difficult to judge cases, and a continuous problem of getting better at filtering out bad cases, such that "if you can tell" isn't a coherent threshold. I'd rather talk about "getting better at distinguishing" reward hacking. I think we just have different implicit baselines here. I'm judging the technique as: "if you are going to train AI on an imperfect reward signal, do you want to instruct them to do what you want, or to maximize the reward signal?" and I think you clearly want the later for simple, elegant reasons. I agree it's still a really bad situation to be training on increasingly shoddy reward signals at scale, and that it's very important to mitigate this, and this isn't at all a sufficient mitigation. I just think it's a principled mitigation.

2habryka1y

I agree with this, but then I don't understand how this solution helps? Like, here we have a case where we can still tell that the environment is being reward hacked, and we tell the model it's fine. Tomorrow the model will encounter an environment where we can't tell that it's reward hacking, so the model will also think it's fine, and then we don't have a feedback loop anymore, and now we just have a model that is happily deceiving us.

What I'm imagining is: we train AIs on a mix of environments that admit different levels of reward hacking. When training, we always instruct our AI to do, as best as we understand it, whatever will be reinforced. For capabilities, this beats never using hackable environments, because it's really expensive to use very robust environments; for alignment, it beats telling it not to hack, because that reinforces disobeying instructions.

In the limit, this runs into problems where we have very limited information about what reward hacking opportunities are pres... (read more)