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Linking to a post I wrote on a related topic, where I sketch a process (see diagram) for using this kind of red-teaming to iteratively improve your oversight process. (I'm more focussed on a scenario where you're trying to offload as much of the work in evaluating and improving your oversight process to AIs)
I read "capable of X" as meaning something like "if the model was actively trying to do X then it would do X". I.e. a misaligned model doesn't reveal the vulnerability to humans during testing bc it doesn't want them to patch it, but then later it exploits that same vulnerability during deployment bc it's trying to hack the computer system
1But realistically not all projects will hoard all their ideas. Suppose instead that for the leading project, 10% of their new ideas are discovered in-house, and 90% come from publicly available discoveries accessible to all. Then, to continue the car analogy, it’s as if 90% of the lead car’s acceleration comes from a strong wind that blows on both cars equally. The lead of the first car/project will lengthen slightly when measured by distance/ideas, but shrink dramatically when measured by clock time.
The upshot is that we should return to that table of factors and add a big one to the left-hand column: Leads shorten automatically as general progress speeds up, so if the lead project produces only a small fraction of the general progress, maintaining a 3-year lead throughout a soft takeoff is (all else equal) almost as hard as growing a 3-year lead into a 30-year lead during the 20th century. In order to overcome this, the factors on the right would need to be very strong indeed.
But won't "ability to get a DSA" be linked to the lead as measured in ideas rather than clock time?
Exciting post!
One quick question:
Shouldn't you choose a goal that goes beyond the length of the episode (like "tell as many users as possible the AI hates them") to give the model an instrumental reason to "play nice" in training. Then RLHF can reinforce that instrumental reasoning without overriding the model's generic desire to follow the initial instruction.