If we replace ‘evil’ with ‘capable and deceptively aligned’, then I think this logic doesn’t hold. Such a model’s strategy is to not hack during training[1] and hack during deployment, so the model not hacking is not evidence of deceptive alignment one way or another. Moreover including the string ‘it’s okay to hack’ wouldn’t change the hack rate of capable deceptively alignment models, especially if they are aware of this as a common alignment technique. So the coefficient of ∇P(deceptively aligned) is ~0.
I’m unsure what you mean. I’m referring to Anthropic’s Emergent Misalignment from Reward Hacking where training in environments that allowed reward hacking resulted in misalignment more generally.
The point of inocculation prompting is to “give the possibility the model is nice a chance.”
Consider this experiment. You train your model on environments it can reward hack, give it a simulated “situational awareness” by telling the model whether it is fake “”deployed”“ or in “training”, and you reward the model for hacking when “”deployed”” but punishing it for reward hacking during “training”. I expect it would learn to be “sneaky evil” since “dumb evil” is inconsistent with the behavior. Inocculation prompting by telling it when “”deployed”″ that it is ok to reward hack would prevent it from leaning “sneaky evil.”
If we replace ‘evil’ with ‘capable and deceptively aligned’, then I think this logic doesn’t hold. Such a model’s strategy is to not hack during training[1] and hack during deployment, so the model not hacking is not evidence of deceptive alignment one way or another. Moreover including the string ‘it’s okay to hack’ wouldn’t change the hack rate of capable deceptively alignment models, especially if they are aware of this as a common alignment technique. So the coefficient of ∇P(deceptively aligned) is ~0.
Or rather, to hack at the same rate as an aligned model.
I’m unsure what you mean. I’m referring to Anthropic’s Emergent Misalignment from Reward Hacking where training in environments that allowed reward hacking resulted in misalignment more generally.
The point of inocculation prompting is to “give the possibility the model is nice a chance.”
Consider this experiment. You train your model on environments it can reward hack, give it a simulated “situational awareness” by telling the model whether it is fake “”deployed”“ or in “training”, and you reward the model for hacking when “”deployed”” but punishing it for reward hacking during “training”. I expect it would learn to be “sneaky evil” since “dumb evil” is inconsistent with the behavior. Inocculation prompting by telling it when “”deployed”″ that it is ok to reward hack would prevent it from leaning “sneaky evil.”