Should we do more research on improving RLHF (e.g. increasing its sample efficiency, or understanding its empirical properties) now?
I think this research, though it’s not my favorite kind of alignment research, probably contributes positively to technical alignment. Also, it maybe boosts capabilities, so I think it’s better to do this research privately (or at least not promote your results extensively in the hope of raising more funding). I normally don’t recommend that people research this, and I normally don’t recommend that projects of this type be funded.
Should we do research on alignment schemes which use RLHF as a building block? E.g. work on recursive oversight schemes or RLHF with adversarial training?
IMO, this kind of research is promising and I expect a large fraction of the best alignment research to look like this.
Where does this difference come from?
From an alignment perspective, increasing the sample efficiency of RLHF looks really good, as this would allow us to use human experts with plenty of deliberation time to provide the supervision. I’d expect this to be at least as good as several rounds of debate, IDA, or RRM in which the human supervision is provided by human lay people under time pressure.
From a capabilities perspective, recursive oversight schemes and adversarial training also increase capabilities. E.g. a reason that Google currently doesn’t deploy their LLMs is probably that they sometimes produce offensive output, which is probably better addressed by increasing the data diversity during finetuning (e.g. adversarial training), rather than improving specification (although that’s not clear).
Where does this difference come from?
From an alignment perspective, increasing the sample efficiency of RLHF looks really good, as this would allow us to use human experts with plenty of deliberation time to provide the supervision. I’d expect this to be at least as good as several rounds of debate, IDA, or RRM in which the human supervision is provided by human lay people under time pressure.
From a capabilities perspective, recursive oversight schemes and adversarial training also increase capabilities. E.g. a reason that Google currently doesn’t deploy their LLMs is probably that they sometimes produce offensive output, which is probably better addressed by increasing the data diversity during finetuning (e.g. adversarial training), rather than improving specification (although that’s not clear).