Thanks for these points! I think I understand the history of what has happened here better now -- and the reasons for my misapprehension. Essentially, what I think happened is
a.) LLM/NLP research always (?) used 'pretraining' for a long time back at least to 2017 era for a general training of a model not specialised for a certain NLP task (such as NER, syntax parsing, etc)
b.) rest of ML mostly used 'training' because they by and by large didn't do massive unsupervised training on unrelated tasks -- i.e. CV just had imagenet or whatever
c.) In 2020-2022 period NLP with transformers went from fairly niche subfield of ML to memetically dominant due to massive success of transformer GPT models
d.) This meant both that their linguistic descriptions of 'pretraining' spread much more widely due to uptake of similar methods in other subfields and that I got much more involved in looking at NLP / LLM research than I had in the past where I personally had focused more on CV and RL leading to its sudden appearance in my personal experience (which turned out to be wrong).
I like this post very much and in general I think research like this is on the correct lines towards solving potential problems with Goodheart's law -- in general Bayesian reasoning and getting some representation of the agent's uncertainty (including uncertainty over our values!) seems very important and naturally ameliorates a lot of potential problems. The correctness and realizability of the prior are very general problems with Bayesianism but often do not thwart its usefulness in practice although they allow people to come up with various convoluted counterexamples of failure. The key is to have sufficiently conservative priors such that you can (ideally) prove bounds about the maximum degree of goodhearting that can occur under realistic circumstances and then translate these into algorithms which are computationally efficient enough to be usable in practice. People have already done a fair bit of work on this in RL in terms of 'cautious' RL which tries to take into account uncertainty in the world model to avoid accidentally falling into traps in the environment.
While I agree with a lot of points of this post, I want to quibble with the RL not maximising reward point. I agree that model-free RL algorithms like DPO do not directly maximise reward but instead 'maximise reward' in the same way self-supervised models 'minimise crossentropy' -- that is to say, the model is not explicitly reasoning about minimising cross entropy but learns distilled heuristics that end up resulting in policies/predictions with a good reward/crossentropy. However, it is also possible to produce architectures that do directly optimise for reward (or crossentropy). AIXI is incomputable but it definitely does maximise reward. MCTS algorithms also directly maximise rewards. Alpha-Go style agents contain both direct reward maximising components initialized and guided by amortised heuristics (and the heuristics are distilled from the outputs of the maximising MCTS process in a self-improving loop). I wrote about the distinction between these two kinds of approaches -- direct vs amortised optimisation here. I think it is important to recognise this because I think that this is the way that AI systems will ultimately evolve and also where most of the danger lies vs simply scaling up pure generative models.
This monograph by Bertsekas on the interrelationship between offline RL and online MCTS/search might be interesting -- http://www.athenasc.com/Frontmatter_LESSONS.pdf -- since it argues that we can conceptualise the contribution of MCTS as essentially that of a single Newton step from the offline start point towards the solution of the Bellman equation. If this is actually the case (I haven't worked through all details yet) then this seems to be able to be used to provide some kind of bound on the improvement / divergence you can get once you add online planning to a model-free policy.
Thanks for writing this! Here are some of my rough thoughts and comments.
One of my big disagreements with this threat model is that it assumes it is hard to get an AGI to understand / successfully model 'human values'. I think this is obviously false. LLMs already have a very good understanding of 'human values' as they are expressed linguistically, and existing alignment techniques like RLHF/RLAIF seem to do a reasonably good job of making the models' output align with these values (specifically generic corporate wokeness for OpenAI/Anthropic) which does appear to generalise reasonably well to examples which are highly unlikely to have been seen in training (although it errs on the side of overzealousness of late in my experience). This isn't that surprising because such values do not have to be specified by the fine-tuning from scratch but should already be extremely well represented as concepts in the base model latent space and merely have to be given primacy. Things would be different, of course, if we wanted to align the LLMs to some truly arbitrary blue and orange morality not represented in the human text corpus, but naturally we don't.
Of course such values cannot easily be represented as some mathematical utility function, but I think this is an extremely hard problem in general verging on impossible -- since this is not the natural type of human values in the first place, which are naturally mostly linguistic constructs existing in the latent space and not in reality. This is not just a problem with human values but almost any kind of abstract goal you might want to give the AGI -- including things like 'maximise paperclips'. This is why almost certainly AGI will not be a direct utility maximiser but instead use a learnt utility function using latents from its own generative model, but in this case it can represent human values and indeed any goal expressible in natural language which of course it will understand.
On a related note this is also why I am not at all convinced by the supposed issues over indexicality. Having the requisite theory of mind to understand that different agents have different indexical needs should be table stakes to any serious AGI and indeed hardly any humans have issues with this, except for people trying to formalise it into math.
There is still a danger of over-optimisation, which is essentially a kind of overfitting and can be dealt with in a number of ways which are pretty standard now. In general terms, you would want the AI to represent its uncertainty over outcomes and utility approximator and use this to derive a conservative rather than pure maximising policy which can be adjusted over time.
I broadly agree with you about agency and consequentialism being broadly useful and ultimately we won't just be creating short term myopic tool agents but fully long term consequentialists. I think the key thing here is just to understand that long term consequentialism has fundamental computational costs over short term consequentialism and much more challenging credit assignment dynamics so that it will only be used where it actually needs to be. Most systems will not be long term consequentialist because it is unnecessary for them.
I also think that breeding animals to do tasks or looking at humans subverting social institutions is not necessarily a good analogy to AI agents performing deception and treacherous turns. Evolution endowed humans and other animals with intrinsic selfish drives for survival and reproduction and arguably social deception which do not have to exist in AGIs. Moreover, we have substantially more control over AI cognition than evolution does over our cognition and gradient descent is fundamentally a more powerful optimiser which makes it challenging to produce deceptive agents. There is basically no evidence for deception occurring with current myopic AI systems and if it starts to occur with long term consequentialist agents it will be due to either a breakdown of credit assignment over long horizons (potentially due to being forced to use worse optimisers such as REINFORCE variants rather than pure BPTT) or the functional prior of such networks turning malign. Of course if we directly design AI agents via survival in some evolutionary sim or explicitly program in Omohundro drives then we will run directly into these problems again.
1This is an interesting idea. I feel this also has to be related to increasing linearity with scale and generalization ability -- i.e. if you have a memorised solution, then nonlinear representations are fine because you can easily tune the 'boundaries' of the nonlinear representation to precisely delineate the datapoints (in fact the nonlinearity of the representation can be used to strongly reduce interference when memorising as is done in the recent research on modern hopfield networks) . On the other hand, if you require a kind of reasonably large-scale smoothness of the solution space, as you would expect from a generalising solution in a flat basin, then this cannot work and you need to accept interference between nearly orthogonal features as the cost of preserving generalisation of the behaviour across many different inputs which activate the same vector.
Looks like I really need to study some SLT! I will say though that I haven't seen many cases in transformer language models where the eigenvalues of the Hessian are 90% zeros -- that seems extremely high.
This is a really good post. Some minor musings:
Perhaps a difference in opinion is that it's really unclear to me that an AGI wouldn't do much the same thing of "thinking about it more, repeatedly querying their 'ground truth' social instincts" that humans do. Arguably models like Claude Opus already do this where it clearly can do detailed reasoning about somewhat out-of-distribution scenarios using moral intuitions that come from somewhere. Where this somewhere is going to be some inscrutable combination of similar scenarios in pretraining data, generalization from humans talking about morality, and intuitions derived from the RLAIF phase which embeds Claude's constitution etc. Of course we can argue that Claude's 'social instincts' derived in this way are defective somehow compared to humans but it is unclear (to me) that this path cannot make AGIs with decent social instincts.
Perhaps a crux of differences in opinion between us is that I think that much more 'alignment relevant' morality is not created entirely by innate human social instincts but is instead learnt by our predictive world models based on external data -- i.e. 'culture'. Now culture itself obviously is downstream of a lot of our social instincts but it is also based on other factors like game-theoretic equilibria which promote cooperation even among selfish agents and, very pertinently, using logical 'system 2' reasoning to try to generalize and extend our inchoate social instincts and then learn to backprop this new understanding into our learnt value functions. Utilitarianism, and this super generalized EA-style compassion it brings is a great example of this. No primitive tribesman or indeed very few humans before the 18th century had ever thought of or had moral intuitions aligned with these ideas. They are profoundly unnatural to our innate 'human social instincts'. (Some) people today feel these ideas viscerally because they have been exposed to them enough that they have propagated them from the world model back into the value function through in-lifetime learning.
We don't have to conjure up thought experiments about aliens outside of our light cone. Throughout most of history humans have been completely uncompassionate about suffering existing literally right in front of their faces. From the beginning of time to the 18th century almost nobody had any issues with slavery despite often living with slaves or seeing slave suffering on a daily basis. Today, only a few people have moral issues with eating meat despite the enormous mountain of suffering it causes to living animals right here on our own planet while eating meat only brings reasonable (and diminishing), but not humongously massive, benefits to our quality of life.
My thinking is that this 'far-mode' and 'literate/language/system2-derived' morality is actually better for alignment and human flourishing in general than the standard set of human social instincts -- i.e. I would prefer a being with the morality of Claude Opus to rule the world rather than a randomly selected human. Alignment is a high bar and ultimately we need to create minds far more 'saintly' than any living human could ever be.
What we then need to do is figure out how to distill this set of mostly good, highly verbal moral intuitions from culture into a value function that the model 'feels viscerally'. Of course reverse-engineering some human social instincts are probably important here -- i.e. our compassion instinct is good if generalized, and even more generally understanding how the combination of innate reward signals in the hypothalamus plus the representations in our world model gets people to feel viscerally about the fates of aliens we can never possibly interact with, is very important to understand.
As a side-note, it's really unclear how good humans are at generalizing at true out-of-distribution moralities. Today's morality likely looks pretty bad from the ancient Egyptian perspective. We are really bad at worshipping Ra and reconciling with our Ba's. It might be the case that, upon sufficient reflection, the Egyptians would come to realize that we are right all along, but of course we would say that in any case. I don't know how to solve this or whether there is in fact any general case solution to any degree of 'out-of-distribution-ness' except just like pure conservatism where you freeze both the values and the representations they are based on.