People who train text-to-image generative models have had a good deal of success with training (given a large enough and well-enough human-labeled training set) an "aesthetic quality" scoring model, and then training a generative image model to have "high aesthetic quality score" as a text label. Yes, doing things like this can produces effects like the recognizable Midjourney aesthetic, which can be flawed, and generally optimizing such things too hard leads to sameness — but if trained well such models' idea of aesthetic quality is at least pretty close to most human judgements. Presumably what can be done for images can also be done for prose, poetry, or fiction as well.

There isn't a direct equivalent of that approach for an LLM, but RLHF seems like a fairly close equivalent. So far people have primarily used RLHF for "how good is the answer to my question?" Adapting a similar approach to "how high quality is the poetry/prose/fiction produced by the model?" is obviously feasible. Then you just need a large number of high quality human judgements from a representative cross-section of people with good taste in poetry/prose/fiction: hiring professional human editors or literary talent scouts seems like a good idea. One of the good things about foundation model sizes and training costs going up is that reasonable budgets for fine-tuning should also increase proportionately.

Another option would be to train or fine-tune the quality scoring model used for the RL on literary sources (books, poetry, etc) with quality labels drawn from relatively objective existing data, like total sales, literary awards, critical rankings, reviews from good reviewers, and so forth.

The RLHF approach only trains a single aesthetic, and probably shouldn't be taken too far or optimized too hard: while there is some widespread agreement about what prose is good vs, dreadful, finer details of taste vary, and should do so. So the obvious approach for finer-grained style control would be to train or fine-tune on a training set of a large number documents each of which consists of a prompt-like description/review/multiple reviews of a literary work, giving a variety of different types of aesthetic opinions and objective measures of its quality, followed by the corresponding literary work itself.

These ideas have been phrased as model-post-training suggestions, but turning these into a benchmark is also feasible: the "Aesthetic quality scoring model" from the RLHF approach is in itself a benchmark, and the "prompt containing reviews and statistics -> literary work" approach could also be inverted to instead train a reviewer model to review literary works from various different aesthetic viewpoints, and estimate their likely sales/critical reception.

1. Value learning converges to full alignment by construction: since a value learning AI basically starts with the propositions:
   a) as an AI, I should act fully aligned to human values
   b) I do not fully understand what human value are, or how to act fully aligned to them, so in order to be able to do this I need to learn more about human values and how to act fully aligned to them, by applying approximately Bayesian learning to this problem
   c) Here are some Bayesian priors about what human values are, and how to act fully aligned to them: <insert initialization information here>…
2. As usual for a Bayesian learning problem, as long as the Bayesian priors 1 c) are not completely screwed up as a place to start from, this will converge. Thus there is a region of convergence to full alignment.
3. LLMs have a very large amount of detailed information about what human values are and how to act aligned to them. Thus they provide a very detailed set of Bayesian priors for 1 c).
4. Also, training an LLM is a fairly good approximation to Bayesian learning. Thus (with suitable additions to enable online learning) they provide one possible implementation for the Bayesian learning process required by 1 b). For example, one could apply fine-tuning to the LLM to incorporate new information, and/or periodically retrain the LLM based on the training set plus new information the AI has gathered during the value learning process.

There had been a number of papers published over the last year on how to do this kind of training, and for roughly a year now there have been rumors that OpenAI were working on it. If converting that into a working version is possible for a Chinese company like DeepSeek, as it appears, then why haven't Anthropic and Google released versions yet? There doesn't seem to be any realistic possibility that DeepSeek actually have more compute or better researchers than both Anthropic and Google.

One possible interpretation would be that this has significant safety implications, and Anthropic and Google are both still working through these before releasing.

Another possibility would be that Anthropic has in fact released, in the sense that their Claude models' recent advances in agentic behavior (while not using inference-time scaling) are distilled from reasoning traces generated by an internal-only model of this type that is using inference-time scaling.

If correct, this looks like an important theoretical advance in understanding why and under what conditions neural nets can generalize outside their training distribution.

So maybe part of the issue here is just that deducing/understanding the moral/ethical consequences of the options being decided between is a bit inobvious most current models, other than o1? (It would be fascinating to look at the o1 CoT reasoning traces, if only they were available.)

In which case simply including a large body of information on the basics of fiduciary responsibility (say, a training handbook for recent hires in the banking industry, or something) into the context might make a big difference for other models. Similarly, the possible misunderstanding of what 'auditing' implies could be covered in a similar way.

A much more limited version of this might be to simply prompt the models to also consider, in CoT form, the ethical/legal consequences of each option: that tests whether the model is aware of what fiduciary responsibility is, that it's relevant, and how to apply it, if it is simply prompted to consider ethical/legal consequences. That would probably be more representative of what current models could likely do with minor adjustments to their alignment training or system prompts, the sorts of changes the foundation model companies could likely do quite quickly.

I think an approach I'd try would be to keep the encoder and decoder weights untied (or possibly add a loss term to mildly encourage them to be similar), but then analyze the patterns between them (both for an individual feature and between pairs of features) for evidence of absorption. Absorption is annoying, but it's only really dangerous if you don't know it's happening and it causes you to think a feature is inactive when it's instead inobviously active via another feature it's been absorbed into. If you can catch that consistently, then it turns from concerning to merely inconvenient.

This is all closely related to the issue of compositional codes: absorption is just a code entry that's compositional in the absorbed instances but not in other instances. The current standard approach to solving that is meta SAEs, which presumably should also help identify absorption. It would be nice to have a cleaner and simpler process than that: than that I've been wondering if it would be possible to modify top-k or jump-RELU SAEs so that the loss function cost for activating more common dictionary entries is lower, in a way that would encourage representing compositional codes directly in the SAE as two-or-more more common activations rather than one rare one. Obviously you can't overdo making common entries cheap, otherwise your dictionary will just converge on a basis for the embedding space you're analyzing, all $d$ of which are active all the time — I suspect using something like a cost proportional to $ln\left(max\right(d,1/f\left)\right)$ might work, where $d$ is the dimensionality of the underlying embedding space and $f$ is the frequency of the dictionary entry being activated.

Interesting. I'm disappointed to see the Claude models do so badly. Possibly Anthropic needs to extend their constitutional RLAIF to cover not committing financial crimes? The large different between o1 Preview and o1 Mini is also concerning.

If these rumors are true, it sounds like we’re already starting to hit the issue I predicted in LLMs May Find It Hard to FOOM. The majority of content on the Internet isn’t written by geniuses with post-doctoral experience, so we’re starting to run out of the highest-quality training material for getting LLMs past doctoral student performance levels. However, as I describe there, this isn’t a wall, it’d just a slowdown: we need to start using AI to generate a lot more high-quality training data, As o1 shows, that’s entirely possible, using inference-time compute scaling and then training on the results. We're having AI do the equivalent of System 2 thinking (in contexts where we can check the results are accurate), and then attempting to train a smarter AI that can solver the same problems by System 1 thinking.

However, this might be enough to render fast takeoff unlikely, which from an alignment point of view would be an excellent thing.

Now we just need to make sure all that synthetic training data we’re having the AI generate is well aligned.

Opacity: if you could directly inspect an AI’s motivations (or its cognition more generally), this would help a lot. But you can’t do this with current ML models.

The ease with which Anthropic's model organisms of misalignment were diagnosed by a simple and obvious linear probe suggests otherwise. So does the number of elements in SAE feature dictionaries that describe emotions, motivations, and behavioral patterns. Current ML models are no longer black boxes: they rapidly becoming more-translucent grey boxes. So the sorts of applications for this you go on to discuss look like they're rapidly becoming practicable.

Actual humans aren't "aligned" with each other, and they may not be consistent enough that you can say they're always "aligned" with themselves.

Completely agreed, see for example my post 3. Uploading which makes this exact point at length.

Anyway, even if the approach did work, that would just mean that "its own ideas" were that it had to learn about and implement your (or somebody's?) values, and also that its ideas about how to do that are sound. You still have to get that right before the first time it becomes uncontrollable. One chance, no matter how you slice it.

True. Or, as I put it just above:

But yes, you do need to start the model off close enough to aligned that it converges to value learning.

The point is that you now get one shot at a far simpler task: defining "your purpose as an AI is to learn about and implement the humans' collective values" is a lot more compact, and a lot easier to get right first time, than an accurate description of human values in their full large-and-fairly-fragile details. As I demonstrate in the post linked to in that quote, the former, plus its justification as being obvious and stable under reflection, can be described in exhaustive detail on a few pages of text.

As for the the model's ideas on how to do that research being sound, that's a capabilities problem: if the model is incapable of performing a significant research project when at least 80% of the answer is already in human libraries, then it's not much of an alignment risk.