Currently, DeepMind's capabilities evals are run on the post-RL*F (RLHF/RLAIF) models and not on the base models. This worries me because RL*F will train a base model to stop displaying capabilities, but this isn't a guarantee that it trains the model out of having the capabilities.
Consider by analogy using RLHF on a chess-playing AI, where the trainers reward it for putting up a good fight and making the trainer work hard to win, but punish it for ever beating the trainer. There are two things to point out about this example:
Running a simple eval on the post-RLHF model would reveal a much lower ELO than if you ran it on the base model, because it would generally find a way to lose. (In this example, you can imagine the red team qualitatively noticing the issue, but the example is an artificially simple one!)
The post-RLHF model still has much of its chess knowledge latently available, in order to put up a good fight across the full range of human ability. Possibly it's even superhuman at chess—I know I'd have to be better than you at chess in order to optimize well for an entertaining game for you. But that won't show up in its ELO.
So it seems to me like running evals on the base model as well as the post-RL*F model is an extremely sensible precaution against (1), and I'd love to be reassured either that this is unnecessary for some really obvious and ironclad reason, or that someone is already working on this.
And I don't have any good suggestion on (2), the idea that RL*F could reinforce a capability while also concealing it.
METR (formerly ARC Evals) included results on base models in their recent work "Measuring the impact of post-training enhancements" ("post-training enhancements"=elicitation). They found that GPT-4-base performed poorly in their scaffold and prompting.
I believe the prompting they used included a large number of few-show examples (perhaps 10?), so it should be a vaguely reasonable setup for base models. (Though I do expect that elicitation which is more specialized to base model would work better.)
I predict that base models will consistently do worse on tasks that labs care about (software engineering, agency, math) then models which have gone through post-training, particularly models which have gone through post training just aimed at improving capabilities and improving the extent to which the model follows instructions (instruction tuning).
My overall sense is that there is plausibly a lot of low hanging fruit in elicitation, but I'm pretty skeptical that base models are a very promising direction.
(Creating more visibility for a comment thread with Rohin Shah.)
Currently, DeepMind's capabilities evals are run on the post-RL*F (RLHF/RLAIF) models and not on the base models. This worries me because RL*F will train a base model to stop displaying capabilities, but this isn't a guarantee that it trains the model out of having the capabilities.
Consider by analogy using RLHF on a chess-playing AI, where the trainers reward it for putting up a good fight and making the trainer work hard to win, but punish it for ever beating the trainer. There are two things to point out about this example:
So it seems to me like running evals on the base model as well as the post-RL*F model is an extremely sensible precaution against (1), and I'd love to be reassured either that this is unnecessary for some really obvious and ironclad reason, or that someone is already working on this.
And I don't have any good suggestion on (2), the idea that RL*F could reinforce a capability while also concealing it.