Ensembling as an AI safety solution is a bad way to spend down our alignment tax—training another model brings you to 2x compute budget, but even in the best case scenario where the other model is a totally independent draw (which in fact it won’t be), you get at most one extra bit of optimization towards alignment.
Chain of thought prompting can be thought of as creating an average speed bias that might disincentivize deception.
A deceptive model doesn’t have to have some sort of very explicit check for whether it’s in training or deployment any more than a factory-cleaning robot has to have a very explicit check for whether it’s in the jungle instead of a factory. If it someday found itself in a very different situation than currently (training), it would reconsider its actions, but it doesn’t really think about it very often because during training it just looks too unlikely.
Ensembling as an AI safety solution is a bad way to spend down our alignment tax—training another model brings you to 2x compute budget, but even in the best case scenario where the other model is a totally independent draw (which in fact it won’t be), you get at most one extra bit of optimization towards alignment.
Chain of thought prompting can be thought of as creating an average speed bias that might disincentivize deception.
A deceptive model doesn’t have to have some sort of very explicit check for whether it’s in training or deployment any more than a factory-cleaning robot has to have a very explicit check for whether it’s in the jungle instead of a factory. If it someday found itself in a very different situation than currently (training), it would reconsider its actions, but it doesn’t really think about it very often because during training it just looks too unlikely.