This is a link-post for a paper I recently read: Pretraining Language Models with Human Preferences, followed by my reactions to this paper.

Reading this paper has significantly reduced my near-term $P (D O O M)$ , and I'd like to explain why. Thus, this is also an alignment proposal. While I don't think what I'm proposing here is a complete solution to aligning a superintelligent ASI, I think it might work well up to at least around a human-level AGI, and even be a useful basis to build on at ASI level (at that level, I'd advocate adding on value learning). It can achieve some of the simpler things that people have been hoping we might get from Interpretability (and for more complex things might also combine well with and even simplify Interpretability, if that can be made to work at scale.) It's also simple, immediately actionable, has a fairly low alignment tax, and best of all, also has lots of useful capabilities effects, so that even a superscalar not very concerned about x-risk might well still want to implement it. [I'm fully aware that many people on the Alignment Forum/Less Wrong disapprove of pointing out anything that helps capabilities — however, in this case fortunately the alignment and capabilities advantages are heavily and obviously entangled, and I see this as the sugar that may help the medicine go down, so I am going to briefly mention it.]

The Paper

Let's start with the paper. The authors experiment with a number of different ways you might train an LLM not to do some form of undesired behavior. For the paper, they chose three simple, well-defined bad behaviors for which they had low-computational cost, high-accuracy classifiers, and which were behaviors simple enough that a fairly small, economical-to-pretrain LLM could reasonably be expected to understand them. They demonstrate that, unlike the common approach of first training a foundation model on the task "learn to autocomplete a large chunk of the web, which includes both good and bad behavior", followed by fine-tuning/RLHF on "now learn to recognize and only do good behavior, not bad", it is a lot more effective to build this control training in from the start during the pretraining (they estimate by around an order of magnitude). So they evaluate five different methods to do that (plus standard pretraining as a control).

The simplest behavior training approach they try is just prefilt...

Academic Papers