“”RLHF and Fine-Tuning havenot workedwell so far. Models are often unhelpful, untruthful, inconsistent, in many ways that had been theorized in the past. We also witness goal misspecification, misalignment, etc. Worse than this, as models become more powerful, we expect more egregious instances of misalignment, as more optimization will push for more and more extreme edge cases and pseudo-adversarial examples.”″
These three links are:
The first is Mysteries of mode collapse, which claims that RLHF (as well as OpenAI’s supervised fine-tuning on highly-rated responses) decreases entropy. This doesn’t seem particularly related to any of the claims in this paragraph, and I haven’t seen it explained why this is a bad thing. I asked on the post but did not get a response.
The second is Discovering language model behaviors with model-written evaluations and shows that Anthropic’s models trained with RLHF have systematically different personalities than the pre-trained model. I’m not exactly sure what claims you are citing, but I think you are making some really wild leaps.
The third is Compendium of problems with RLHF, which primarily links to the previous 2 failures and then discusses theoretical limitations.
I think these are bad citations for the claim that methods are “not working well” or that current evidence points towards trouble.
The current problems you list—”unhelpful, untruthful, and inconsistent”—don’t seem like good examples to illustrate your point. These are mostly caused by models failing to correctly predict which responses a human would rate highly. That happens because models have limited capabilities and is rapidly improving as models get smarter. These are not the problems that most people in the community are worried about, and I think it’s misleading to say this is what was “theorized” in the past.
I think RLHF is obviously inadequate for aligning really powerful models, both because you cannot effectively constrain a deceptively aligned model and because human evaluators will eventually not be able to understand the consequences of proposed actions. And I think it is very plausible that large language models will pose serious catastrophic risks from misalignment before they are transformative (it seems very hard to tell). But I feel like this post isn’t engaging with the substance of those concerns or sensitive to the actual state of evidence about how severe the problem looks like it will be or how well existing mitigations might work.
Comment by Paul Christiano on Lesswrong: