I think I essentially agree with respect to your definition of “sanity,” and that it should be a goal. For example, just getting people to think more about tail risk seems like your definition of “sanity” and my definition of “safety culture.” I agree that saying that they support my efforts and say applause lights is pretty bad, though it seems weird to me to discount actual resources coming in.
As for the last bit: trying to figure out the crux here. Are you just not very concerned about outer alignment/proxy gaming? I think if I was totally unconcerned about that, and only concerned with inner alignment/deception, I would think those areas were useless. As it is, I think a lot of the work is actively harmful (because it is mostly just advances capabilities) but it still may help chip away at the proxy gaming problem.
I think RLHF doesn’t make progress on outer alignment (like, it directly creates a reward for deceiving humans). I think the case for RLHF can’t route through solving outer alignment in the limit, but has to route through somehow making AIs more useful as supervisors or as researchers for solving the rest of the AI Alignment problem.
Like, I don’t see how RLHF “chips away at the proxy gaming problem”. In some sense it makes the problem much harder since you are directly pushing on deception as the limit to the proxy gaming problem, which is the worst case for the problem.
I’m not a big supporter of RLHF myself, but my steelman is something like:
RLHF is a pretty general framework for conforming a system to optimize for something that can’t clearly be defined. If we just did, “if a human looked at this for a second would they like it?” this does provide a reward signal towards deception, but also towards genuinely useful behavior. You can take steps to reduce the deception component, for example by letting the humans red team the model or do some kind of transparency; this can all theoretically fit in the framework of RLHF. One could try to make the human feedback as robust as possible by adding all sorts of bells and whistles, and this would improve reliability and reduce the deception reward signal. It could be argued this still isn’t sufficient because the model will still find some way around it, but beware too much asymptotic reasoning.
I personally do not find RLHF very appealing since I think it complexifies things unnecessarily and is too correlated with capabilities at the moment. I prefer approaches to actually try to isolate the things people actually care about (i.e. their values), add some level of uncertainty (moral uncertainty, etc.) to it, try to make these proxies as robust as possible, and make them adaptive to changes in the models that are trying to constantly exploit and Goodhart them.
I think I essentially agree with respect to your definition of “sanity,” and that it should be a goal. For example, just getting people to think more about tail risk seems like your definition of “sanity” and my definition of “safety culture.” I agree that saying that they support my efforts and say applause lights is pretty bad, though it seems weird to me to discount actual resources coming in.
As for the last bit: trying to figure out the crux here. Are you just not very concerned about outer alignment/proxy gaming? I think if I was totally unconcerned about that, and only concerned with inner alignment/deception, I would think those areas were useless. As it is, I think a lot of the work is actively harmful (because it is mostly just advances capabilities) but it still may help chip away at the proxy gaming problem.
I think RLHF doesn’t make progress on outer alignment (like, it directly creates a reward for deceiving humans). I think the case for RLHF can’t route through solving outer alignment in the limit, but has to route through somehow making AIs more useful as supervisors or as researchers for solving the rest of the AI Alignment problem.
Like, I don’t see how RLHF “chips away at the proxy gaming problem”. In some sense it makes the problem much harder since you are directly pushing on deception as the limit to the proxy gaming problem, which is the worst case for the problem.
I’m not a big supporter of RLHF myself, but my steelman is something like:
RLHF is a pretty general framework for conforming a system to optimize for something that can’t clearly be defined. If we just did, “if a human looked at this for a second would they like it?” this does provide a reward signal towards deception, but also towards genuinely useful behavior. You can take steps to reduce the deception component, for example by letting the humans red team the model or do some kind of transparency; this can all theoretically fit in the framework of RLHF. One could try to make the human feedback as robust as possible by adding all sorts of bells and whistles, and this would improve reliability and reduce the deception reward signal. It could be argued this still isn’t sufficient because the model will still find some way around it, but beware too much asymptotic reasoning.
I personally do not find RLHF very appealing since I think it complexifies things unnecessarily and is too correlated with capabilities at the moment. I prefer approaches to actually try to isolate the things people actually care about (i.e. their values), add some level of uncertainty (moral uncertainty, etc.) to it, try to make these proxies as robust as possible, and make them adaptive to changes in the models that are trying to constantly exploit and Goodhart them.
This has also motivated me to post one of my favorite critiques of RLHF.