As a baseline, developers could train agents to imitate the truth-seeking process of the most reasonable humans on Earth. For example, they could sample the brightest intellects from every ideological walk, and train agents to predict their actions.
I'm very excited about strategies that involve lots of imitation learning on lots of particular humans. I'm not sure if imitated human researchers learn to generalize to doing lots of novel research, but this seems great for examining research outputs of slightly-more-alien agents very quickly.
This is very interesting! One thing I'm confused about—I agree that the monitor would have a hard time getting the bits, based on these figures. But the introspective models also don't seem to get far above 50% accuracy, which means that right now they'd have a very hard time error correcting enough that their comrade would understand them but not the overseer. If the claim is that introspection-tuned models will get much better at self prediction, then its not clear to me that other models wont also get much better at cross-prediction?
I think that solving the alignment for EV maximizers is a much stronger version of alignment than eg prosaic alignment of LLM-type models. Agents seem like they’ll be more powerful than Tool AIs. We don’t know how to make them, but if someone does, and capabilities timelines shorten drastically, it would be awesome to even have a theory of EV maximizer alignment before then
Hi, my name is Joe. I live in North Jersey. I was born into a very religious Orthodox Jewish family. I only recently realized I how badly I was doublethinking.
I started with HPMOR (as, it seems, do most people) and found my way into the Sequences. I read them all on OB, and was amazed at how eloquently someone else could voice what seems to be my thoughts. It laid out bare the things I had been struggling with.
Then I found LW and was mostly just lurking for a while. I only made an account when I saw this post and realized how badly I wanted to upvote som...
To me this doesn't seem like a failure of sophisticated reward models, it's the failure of unsophisticated reward models (unit tests) when they're being optimized against. I think that if we were to add some expensive evaluation during RL whereby 3.6 checked if 3.7 was "really doing the work", this sort of special-casing would get totally trained out.
(Not claiming that this is always the case, or that models couldn't be deceptive here, or that e.g. 3.8 couldn't reward hack 3.7)