weak to strong generalization is a class of approaches to ELK which relies on generalizing a “weak” supervision signal to more difficult domains using the inductive biases and internal structure of the strong model.
You could also distinguish between weak-to-strong generalization, where you have a weak supervision signal on the entire distribution (which may sometimes be wrong), and easy-to-hard generalization, where you have a correct supervision signal but only on an easy part of the distribution. Of course both of these are simplifications. In reality, I’d expect the setting to be more like: you have a certain weak supervision budget (or maybe even budgets at different levels of strength), and you can probably decide how to spend the budget. You might only have an imperfect sense of which cases are “easy” vs “hard” though.
mechanistic anomaly detection is an approach to ELK
I think going from MAD to a fully general ELK solution requires some extra ingredients. In practice, the plan might be to MTD and then using the AI in ways such that this is enough (rather than needing a fully general ELK solution). This is related to narrow elicitation though MTD seems even narrower. Even for MTD, you probably need something to bridge the easy-to-hard gap, but at least for that there are specific proposals that seem plausible (this or, as a more concrete instance, exclusion fine-tuning from the Redwood MTD paper). I think it could turn out that general/worst-case solutions to MAD and ELK run into very similar obstacles, but I don’t think a practical MAD solution (e.g. contingent on empirical facts about deep learning) obviously lets you solve ELK.
I would also add that you could motivate MAD as a method to deal with scheming (or other high-stakes failures). In that case, the things to compare against most naturally might look a bit different (e.g. AI control, coup probes, interpretability-themed things); and it wouldn’t make sense to compare against debate in that setting. I think most mainstream ML problems that are similar to MAD are closer to this than to scalable oversight.
Nice overview, agree with most of it!
You could also distinguish between weak-to-strong generalization, where you have a weak supervision signal on the entire distribution (which may sometimes be wrong), and easy-to-hard generalization, where you have a correct supervision signal but only on an easy part of the distribution. Of course both of these are simplifications. In reality, I’d expect the setting to be more like: you have a certain weak supervision budget (or maybe even budgets at different levels of strength), and you can probably decide how to spend the budget. You might only have an imperfect sense of which cases are “easy” vs “hard” though.
I think going from MAD to a fully general ELK solution requires some extra ingredients. In practice, the plan might be to MTD and then using the AI in ways such that this is enough (rather than needing a fully general ELK solution). This is related to narrow elicitation though MTD seems even narrower. Even for MTD, you probably need something to bridge the easy-to-hard gap, but at least for that there are specific proposals that seem plausible (this or, as a more concrete instance, exclusion fine-tuning from the Redwood MTD paper). I think it could turn out that general/worst-case solutions to MAD and ELK run into very similar obstacles, but I don’t think a practical MAD solution (e.g. contingent on empirical facts about deep learning) obviously lets you solve ELK.
I would also add that you could motivate MAD as a method to deal with scheming (or other high-stakes failures). In that case, the things to compare against most naturally might look a bit different (e.g. AI control, coup probes, interpretability-themed things); and it wouldn’t make sense to compare against debate in that setting. I think most mainstream ML problems that are similar to MAD are closer to this than to scalable oversight.