Overall, I think imitative amplification seems safer, but I maybe don’t think the distinction is as clear cut as my impression of this post gives.
if you can instruct them not to do things like instantiate arbitrary Turing machines
I think this and “instruct them not to search over arbitrary text strings for the text string that gives the most approval”, and similar things, are the kind of details that would need to be filled out to make the thing you are talking about actually be in a distinct class from approval-based amplification and debate (My post on imitation and RL amplification was intended to argue that without further restrictions, imitation amplification is in the same class as approval-based amplification, which I think we’d agree on). I also think that specifying these restrictions in a way that still lets you build a highly capable system could require significant additional alignment work (as in the Overseer’s Manual scenario here)
Conversely, I also think there are ways that you can limit approval-based amplification or debate—you can have automated checks, for example, that discard possible answers that are outside of a certain defined safe class (e.g. debate where each move can only be from either a fixed library of strings that humans produced in advance or single direct quotes from a human-produced text). I’d also hope that you could do something like have a skeptical human judge that quickly discards anything they don’t understand + an ML imitation of the human judge that discards anything outside of the training distribution (don’t have a detailed model of this, so maybe it would fail in some obvious way)
I think I do believe that for problems where there is a imitative amplification decomposition that solves the problem without doing search, that’s more likely to be safe by default than approval-based amplification or debate. So I’d want to use imitative amplification as much as possible, falling back to approval only if needed. On imitative amplification, I’m more worried that there are many problems it can’t solve without doing approval-maximizing search, which brings the old problems back in again. (e.g. I’m not sure how to use imitative amplification at the meta-level to produce better decomposition strategies than humans use without using approval-based search)
Overall, I think imitative amplification seems safer, but I maybe don’t think the distinction is as clear cut as my impression of this post gives.
I think this and “instruct them not to search over arbitrary text strings for the text string that gives the most approval”, and similar things, are the kind of details that would need to be filled out to make the thing you are talking about actually be in a distinct class from approval-based amplification and debate (My post on imitation and RL amplification was intended to argue that without further restrictions, imitation amplification is in the same class as approval-based amplification, which I think we’d agree on). I also think that specifying these restrictions in a way that still lets you build a highly capable system could require significant additional alignment work (as in the Overseer’s Manual scenario here)
Conversely, I also think there are ways that you can limit approval-based amplification or debate—you can have automated checks, for example, that discard possible answers that are outside of a certain defined safe class (e.g. debate where each move can only be from either a fixed library of strings that humans produced in advance or single direct quotes from a human-produced text). I’d also hope that you could do something like have a skeptical human judge that quickly discards anything they don’t understand + an ML imitation of the human judge that discards anything outside of the training distribution (don’t have a detailed model of this, so maybe it would fail in some obvious way)
I think I do believe that for problems where there is a imitative amplification decomposition that solves the problem without doing search, that’s more likely to be safe by default than approval-based amplification or debate. So I’d want to use imitative amplification as much as possible, falling back to approval only if needed. On imitative amplification, I’m more worried that there are many problems it can’t solve without doing approval-maximizing search, which brings the old problems back in again. (e.g. I’m not sure how to use imitative amplification at the meta-level to produce better decomposition strategies than humans use without using approval-based search)