Beren Millidge

Interested in many things. I have a personal blog at

Wiki Contributions


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. 

afaict, a big fraction of evolution's instructions for humans (which made sense in the ancestral environment) are encoded as what you pay attention to. Babies fixate on faces, not because they have a practical need to track faces at 1 week old, but because having a detailed model of other humans will be valuable later. Young children being curious about animals is a human universal. Etc.

This is true but I don't think is super important for this argument. Evolution definitely encodes inductive biases into learning about relevant things which ML architectures do not, but this is primarily to speed up learning and handle limited initial data. Most of the things evolution focuses on such as faces are natural abstractions anyway and would be learnt by pure unsupervised learning systems.

Patterns of behavior (some of which I'd include in my goals) encoded in my model can act in a way that's somewhere between unconscious and too obvious to question - you might end up doing things not because you have visceral feelings about the different options, but simply because your model is so much better at some of the options that the other options never even get considered. 

Yes, there are also a number of ways to short-circuit model evaluation entirely. The classic one is having a habit policy which is effectively your action prior. There are also cases where you just follow the default model-free policy and only in cases where you are even more uncertain do you actually deploy the full model-based evaluation capacities that you have.

I always say that the whole brain (including not only the basal ganglia but also the thalamocortical system, medulla, etc.) operates as a model-based RL system. You’re saying that the BG by itself operates as a model-free RL system. So I don’t think we’re disagreeing, because “the cortex is the model”?? (Well, we definitely have some disagreements about the BG, but we don’t have to get into them, I don’t think they’re very important for present purposes.)

I think there is some disagreement here, at least in the way I am using model-based / model-free RL (not sure exactly how you are using it). Model-based RL, at least to me, is not just about explicitly having some kind of model, which I think we both agree exists in cortex, but rather the actual action selection system using that model to do some kind of explicit rollouts for planning. I do not think the basal ganglia does this, while I think the PFC has some meta-learned ability to do this. In this sense, the BG is 'model-free' while the cortex is 'model-based'. 

I don’t really find “meta-RL” as a great way to think about dlPFC (or whatever the exact region-in-question is). See Rohin’s critique of that DeepMind paper here. I might instead say that “dlPFC can learn good ideas / habits that are defined at a higher level of abstraction” or something like that. For example, if I learn through experience (or hearsay) that it’s a good idea to use Anki flashcards, you can call that Meta-RL (“I am learning how to learn”). But you can equally well describe it as “I am learning to take good actions that will eventually lead to good consequences”. Likewise, I’d say “learning through experience that I should suck up to vain powerful people” is probably is in the same category as “learning through experience that I should use Anki flashcards”—I suspect they’re learned in the same way by the same part of PFC—but “learning to suck up” really isn’t the kind of thing that one would call “meta-RL”, I think. There’s no “meta”—it’s just a good (abstract) type of action that I have learned by RL.

This is an interesting point. At some level of abstraction, I don't think there is a huge amount of difference between meta-RL and 'learning highly abstract actions/habits'. What I am mostly pointing towards this is the PFC learns high-level actions including how to optimise and perform RL over long horizons effectively including learning high-level cognitive habits like how to do planning etc, which is not an intrinsic ability but rather has to be learned. My understanding of what exactly the dlPFC does and how exactly it works is the place where I am most uncertain at present.

I agree in the sense of “it’s hard to look at the brainstem and figure out what a developed-world adult is trying to do at any given moment, or more generally in life”. I kinda disagree in the sense of “a person who is not hungry or cold will still be motivated by social status and so on”. I don’t think it’s right to put “eating when hungry” in the category of “primary reward” but say that “impressing one’s friends” is in a different, lesser category (if that’s what you’re saying). I think they’re both in the same category.

I agree that even when not immediately hungry or cold etc we still get primary rewards from increasing social status etc. I don't completely agree with Robin Hanson that almost all human behaviour can be explained by this drive directly though. I think we act on more complex linguistic values, or at least our behaviour to fulfil these primary rewards of social status is mediated through these. 

I don’t particularly buy the importance of words-in-particular here. For example, some words have two or more definitions, but we have no trouble at all valuing one of those definitions but not the other. And some people sometimes have difficulty articulating their values. From what I understand, internal monologue plays a bigger or smaller role in the mental life of different people. So anyway, I don’t see any particular reason to privilege words per se over non-linguistic concepts, at least if the goal is a descriptive theory of humans. If we’re talking about aligning LLMs, I’m open to the idea that linguistic concepts are sufficient to point at the right things.

So for words literally, I agree with this. By 'linguistic' I am more pointing at abstract high-level cortical representations. I think that for the most part these line up pretty well with and are shaped by our linguistic representations and that the ability of language to compress and communicate complex latent states is one of the big reasons for humanity's success. 

I think I would have made the weaker statement “There is no particular reason to expect this project to be possible at all.” I don’t see a positive case that the project will definitely fail. Maybe the philosophers will get very lucky, or whatever. I’m just nitpicking here, feel free to ignore.

This is fair. I personally have very low odds on success but it is not a logical impossibility. 

I think (?) you’re imagining a different AGI development model than me, one based on LLMs, in which more layers + RLHF scales to AGI. Whereas I’m assuming (or at least, “taking actions conditional on the assumption”) that LLM+RLHF will plateau at some point before x-risk, and then future AI researchers will pivot to architectures more obviously & deeply centered around RL, e.g. AIs for which TD learning is happening not only throughout training but also online during deployment (as it is in humans).

I am not sure we actually imagine that different AGI designs. Specifically, my near-term AGI model is essentially a multi-modal DL-trained world model, likely with an LLM as a centrepiece but also potentially vision and other modalities included, and then trained with RL either end to end or as some kind of wrapper on a very large range of tasks. I think, given that we already have extremely powerful LLMs in existence, almost any future AGI design will use them at least as part of the general world model. In this case, then there will be a very general and highly accessible linguistic latent space which will serve as the basis of policy and reward model inputs. 

1. Evolution needed to encode not only drives for food or shelter, but also drives for evolutionary desirable states like reproduction; this likely leads to drives which are present and quite active, such as "seek social status" => as a consequence I don't think the evolutionary older drives are out of play and the landscape is flat as you assume, and dominated by language-model-based values

Yes, I think drives like this are important on two levels. At the first level, we are experience them as primary rewards -- i.e. as social status gives direct dopamine hits. Secondly, they shape the memetic selection environment which creates and evolves linguistic memes of values. However, it's important to note that almost all of these drives such as for social status are mediated through linguistic cortical abstractions. I.e. people will try to get social status by fulfilling whatever the values of their environment are, which can lead to very different behaviours being shown and rewarded in different environments, even though powered by the same basic drive. 

 3. The world model isn't a value-indepedent goal-orthogonal model; the stuff it learned is implicitly goal-oriented by being steered by the reward model

The world model is learnt mostly by unsupervised predictive learning and so is somewhat orthogonal to the specific goal. Of course in practice in a continual learning setting, what you do and pay attention to (which is affected by your goal) will affect the data input to the unsupervised learning process? 

Also, in my impression, these 'verbal' values sometimes seem to basically hijack some deeper drive and channel it to meme-replicating efforts. ("So you do care? And have compassion? That's great - here is language-based analytical framework which maps your caring onto this set of symbols, and as a consequence, the best way how to care is to do effective altruism community building")

This is definitely true for humans but it is unclear that this is necessarily bad. This is at least somewhat aligned and this is how any kind of intrinsic motivation to external goals has to work -- i.e. the external goal gets supported by and channels an intrinsic motivation. 

5. I don't think that "when asked, many humans want to try to reduce the influence of their ‘instinctual’ and habitual behaviours and instead subordinate more of their behaviours to explicit planning" is much evidence of anything. My guess is actually many humans would enjoy more of the opposite - being more embodied, spontaneous, instinctive, and this is also true for some of the smartest people around. 

Yeah,  in the post I say I am unclear as to whether this is stable under reflection. I see alignment techniques that would follow from this as being only really applicable to near-term systems and not under systems undergoing strong RSI.

6. Broadly, I don't think the broad conclusion human values are primarily linguistic concepts encoded via webs of association and valence in the cortex learnt through unsupervised (primarily linguistic) learning is stable upon reflection. 


My understanding is that after a lot of simplifications, policy gradients just takes a noisy gradient step in the direction of minimising Bellman error, and so in the limit of infinite data/computation/visiting all states in the world, it is 'guaranteed' to converge to an optimal policy for the MDP. Q learning and other model-free algorithms have similar guarantees. In practice, with function approximation, and PPOs regularisation bits, these guarantees do not hold anymore, but the fundamental RL they are built off of does have them. The place to go deeper into this is Sutton and Bart's textbook and also Bertsekas' dynamic programming textbook

I broadly agree with a lot of shard theory claims. However, the important thing to realise is that 'human values' do not really come from inner misalignment wrt our innate reward circuitry but rather are the result of a very long process of social construction influenced both by our innate drives but also by the game-theoretic social considerations needed to create and maintain large social groups, and that these value constructs have been distilled into webs of linguistic associations learnt through unsupervised text-prediction-like objectives which is how we practically interact with our values. Most human value learning occurs through this linguistic learning grounded by our innate drives but extended to much higher abstractions by language.i.e. for humans we learn our values as some combination of bottom-up (how well do our internal reward evaluators in basal ganglia/hypothalamus) accord with the top-down socially constructed values) as well as top-down association of abstract value concepts with other more grounded linguistic concepts.

With AGI, the key will be to work primarily top-down since our linguistic constructs of values tend to reflect much better our ideal values than our actually realised behaviours. Using the AGI's 'linguistic cortex' which already has encoded verbal knowledge about human morality and values to evaluate potential courses of action and as a reward signal which can then get crystallised into learnt policies. The key difficulty is understanding how, in humans, the base reward functions interact with behaviour to make us 'truly want' specific outcomes (if humans even do) as opposed to reward or their correlated social assessments. It is possible, even likely, that this is just the default outcome of model-free RL experienced from the inside and in this case our AGIs would look highly anthropomorphic.

Also in general I disagree about aligning agents to evaluations of plans being unnecessary. What you are describing here is just direct optimization. But direct optimization  -- i.e .effectively planning over a world model -- is necessary in situations where a.) you can't behaviourally clone existing behaviour and b.) you can't self-play too much with a model-free RL algorithms and so must rely on the world-model. In such a scenario you do not have ground truth reward signals and the only way to amake progresss is to optimise against some implicit learnt reward function. 

I also am not sure that an agent that explicitly optimises this is hard to align and the major threat is goodhearting. We can perfectly align Go-playing AIs with this scheme because we have a ground truth exact reward function. Goodhearting is essentially isomorphic to a case of overfitting and can in theory be solved with various kinds of regularisation, especially if the AI maintains a well-calibrated sense of reward function uncertainty then in theory we can derive quantification bounds on its divergence from the true reward function. 

I feel like this is a good point in general but I think there is an important but subtle distinction between the two examples. In the first case of the GAN it is that there is the distinction between the inner optimization loop of the ML algorithm and the outer loop of humans performing an evolutionary search process to get papers/make pretty pictures. 


In the wire-heading case this feels different in that you have essentially two separate value functions -- a cortical LM based one which can extrapolate values in linguistic/concept space and a classic RL basal-ganglia value function which is based on your personal experience. The difference here is mostly in training data -- the cortex is trained on a large sensory corpus including linguistic text describing wire heading. The subcortical value function is largely trained on personal rewarding experiences. It would be odd to have them necessarily be always consistent and would lead to strange failure modes exactly like wire heading, or generally being able to be viscerally convinced of anything you read that sounds convincing.