I don’t have formal education on computer science, but my feeling is that the paper doesn’t falsify your idea.
I think that transformers are very good at learning a ton of different algorithms for ton of different tasks. E.g. they might learn to use the clock algorithm to do addition. They probably also learn algorithms to translate languages with different grammatical rules.
I think the paper just demonstrates that one of these algorithms emulates gradient descent (or a version of it), and that the AI uses this algorithm for linear regression problems.
But just because the AI is capable of running this gradient descent algorithm in a single pass, doesn’t mean all of the AI’s behaviours are gradient descent products of some mesa-optimizer goal (completely different than the reward function).
In fact, I still think most of the AI’s single pass behaviours are gradient descent products of the outer optimization loop (i.e. the reward function or pretraining), just like you predict.
But
Although I don’t think that study falsifies your argument, I’m still unsure about one part of your argument:
Now, why wouldn’t neural networks have this same disposition for inner, looping algorithms? Well, remember that deep learning analogue to genes is supposed to be weights. However, unlike genes, weights are only used once each over the course of a given forward pass (itself the deep learning analogue to a human lifetime).[8]This means that in order for a neural network to implement, say, repeated evaluations of different ideas for the actions it might take, its weights would need to be configured to implement the same evaluation at many different stages of the neural network’s data processing procedure. This would seem to require an incredibly gerrymandered setup, one that seems unlikely to arise in the normal course of gradient descent.
Modern AI do a long chain-of-thought to search for the best output. This consists of very many forward passes. Doesn’t this looks like a looping algorithm to you?
One analogy is that for a single neuron firing, its behaviour has only been optimized by the outer optimization loop (evolution). But the thoughts represented by many neuron firings is optimizing towards a mesa-optimizer goal like “eat delicious food,” without any regard towards evolutionary fitness.
Anyways
Thank you for sharing this, and thank you so much for being willing to admit mistake. That is very hard and you are probably much better at it than me haha.
Modern AI do a long chain-of-thought to search for the best output. This consists of very many forward passes. Doesn’t this looks like a looping algorithm to you?
Yes, and relatedly LLMs are run in loops just to generate more than one token in general. This is different than running an explicit optimization algorithm within a single forward pass.
Anyway, the part of my post the paper falsifies is that it’s forbiddingly difficult for neural networks to implement explicit, internal optimization algorithms. I don’t think the paper is strong evidence that all of a trained transformer’s outputs are generated primarily/exclusively by means of such an algorithm, and running gradient descent with a predictive objective internally sounds a lot less dangerous than running a magically functional AIXI approximation internally anyway. So there are still major assumptions made by RFLO that haven’t born out in reality yet.
The fundamental idea about genes having an advantage over weights at internally implementing looping algorithms is apparently wrong though (even though I don’t understand how the contrary is possible...).
The fundamental idea about genes having an advantage over weights at internally implementing looping algorithms is apparently wrong though (even though I don’t understand how the contrary is possible...)
I’ve been trying to understand this myself. Here’s a the understanding I’ve come to, which is very simplistic. If someone who knows more about transformers than me says I’m wrong I will defer to them.
In order to have a mesa-optimizer, lots and lots of layers need to be in on the game of optimization, rather than just one or several key elements which gets referenced repeatedly during the optimization process.
But self-attention is, by default, not very far away from being one step in gradient descent. Every layer doesn’t need to learn to do optimization independently from scratch, since it’s relatively easy to find given the self-attention architecture.
That’s why it’s not forbiddingly difficult for neural networks to implement internal optimization algorithms. It still could be forbiddingly difficult for most optimization algorithms, ones that aren’t easy to find from the basic architecture.
if you have a more detailed grasp on how exactly self-attention is close to a gradient descent step please do let me know, i’m having a hard time making sense of the details of these papers
Note that if computing an optimization step reduces the loss, the training process will reinforce it, even if other layers aren’t doing similar steps, so this is another reason to expect more explicit optimizers.
Basically, self attention is a function of certain matrices, something like this:
ej←ej+∑hPhWh,V∑ieh,i⊗eh,iWTh,KWh,Qej
Which looks really messy when you put it like this but is pretty natural in context.
If you can get the big messy looking term to approximate a gradient descent step for a given loss function, then you’re golden.
In appendix A.1., they show the matrices that yield this gradient descent step. They are pretty simple, and probably an easy point of attraction to find.
All of this reasoning is pretty vague, and without the experimental evidence it wouldn’t be nearly good enough. So there’s definitely more to understand here. But given the experimental evidence I think this is the right story about what’s going on.
Why do you think genes don’t have an advantage over weights for mesaoptimization? The paper shows that weights can do it but mightn’t genes still have an advantage?
I didn’t follow the details, I’m just interested in the logic you’re applying to conclude that your theoretical work was invalidated by that empirical study.
I also think Yudkowsky’s argument isn’t just about mesaoptimizers. You can have the whole network optimize for the training set, and just be disappointed to find out that it didn’t learn what you’d hoped it would learn when it gets into new environments. If we imagined that evolution was a person named Evie, she’d think her training technique worked great. If she came back now and saw human population declining from birth control use and shifting cultural values, shed realize it didn’t work nearly as well as she’d thought from evidence in the ancestral environment because they were optizing for the sex part and not the reproduction part. Steve Byrnes as usual has a very lucid breakdown of this logic. That’s not mesaoptimization or inner misalignment, just alignment misgeneralization. I think any algorithm strong enough to optimize anything might be optimizing something different than it’s trainer hoped when they built the training environment.
Mesaoptimization is one way to get misalignment but not the only way.
So supposing that’s all roughly correct, what about the lesson to be learned in research or theory development? I think you’ve drawn the correct one, but in the wrong direction: Doing more lit review saves time and heartache, but reviewing different ways of viewing the question you’re trying to address with your theory is at least as important as reviewing for empirical evidence.
That was my conclusion after doing cognitive neuroscience theory for a long time, and observing others doing it. There was a tendency to put a bunch of work into developing and writing up your theory, then to make the last step before publication doing a thorough lit review to make sure you didn’t look like an idiot when you published. Or not doing that and having one of the reviewers say something like “ummm I don’t think your interpretation of that theory was what the author meant, so you’re fighting a straw man here...”
At that point, people would be so invested that they’d do intellectual backflips to twist the interpretation around to publish that work (they’d perish if they gave up a year of work very often). Sometimes it could be reframed in light of the evidence or better understanding of existing theory, sometimes it wound up being basically counterproductive since the incentive was to fool yourself, which required writing something convincing enough to fool a bunch of other people.
Thanks for not doing that! It is so good to be in a community with strong values of epistemic rigor and honesty with ones self and others. Many kudos to you for withdrawing the piece instead of pushing on—and then publishing it with the admission of what went wrong for others to learn from.
in the section of the post i didn’t finish and therefore didn’t include here, i talk about how like… okay so valuing some outcome is about reliably taking actions which increase the subjective probability of that outcome occurring. explicit utility maximizers are constantly doing this by nature, but systems that acquire their values via RL (such as humans and chat models) only do so contextually and imperfectly. like… the thing RL fundamentally is, is a way of learning to produce outputs that predictably get high reward from the loss function. this only creates systems which optimize over the external world to the extent that, in certain situations, the particular types of actions a model learns happen to tend to steer the future in particular directions. so… failures of generalization here don’t necessarily result in systems that optimize effectively for anything at all; their misgeneralized behavior can in principle just be noise, and indeed it typically empirically is in deep learning, e.g. memorizing the training data.
(see also the fact that e.g. claude sometimes steers the future from certain “tributary states” like the user asking it for advice towards certain attractor basisns, like the user making a good decision. claude does this reliably despite not trying to optimize the cosmos for something else besides that. and it’s hard to imagine concretely what a “distributional shift” that would cause asked-for-advice claude to start reliably giving bad advice; maybe if the user has inhuman psychology, i guess? such that claude’s normal advice was bad? idk. i suppose claude can be prompted to be a little bit malicious if you really know what you’re doing, which can “steer the world” towards mildly but still targetedly bad outcomes given certain input states...)
anyway, humans are examples of systems that do somewhat effectively optimize for things other than what they were trained to optimize for, but that’s an artifact of the particular methods natural selection bestowed upon us for maximizing inclusive genetic fitness (namely a specific effective RL-ish setup). in this post, i was trying to argue that certain classes of setups that do reliably produce that kind of outcome, such as a subset of explicit optimization algorithms, are unlikely under gradient descent. but apparently it’s just not actually that hard to build explicit optimization algorithms under gradient descent. so, poof goes the argument.
Did the paper really show that they’re explicit optimizers? If so, what’s your definition of them?
I have representations of future states and I choose actions that might lead toward them. Those representations of my goals are explicit, so I’d call myself an explicit optimizer.
I added a bunch to the previous comment in an edit, sorry! I was switching from phone to laptop when it got longer. So you might want to look at it again to see if you got the full version.
“explicit optimizer” here just means that you search through some space of possibilities, and eventually select one that scores high according to some explicit objective function. (this is also how MIRI’s RFLO paper defines optimization.) the paper strongly suggests that neural networks sometimes run something like gradient descent internally, which fits this definition. it’s not necessarily about scheming to reach long-term goals in the external world, though that’s definitely a type of optimization.
(it’s clear that Claude etc. can do that kind of optimization verbally, i.e. not actually within its own weights; it can think through multiple ideas for action, rank them, and pick the best one too. the relevant difference between this and paperclip-style optimization is that its motivation to actually pursue any given goal is dependent on its weights; you could totally prompt an LLM to with a natural language command to pursue some goal, but it refuses because it’s been trained to not pursue such goals. and this relates to the things where like… at the layer of natural language processing anyway, your verbally thought “goals” are more like attempts to steer a fuzzy inference process, which itself may or may not have an explicit internal representation of end-state it’s actually aiming at. if not, the yudkowsian image of utility maximization becomes misleading, and there’s no longer reason to expect the system to be “trying” to steer the system towards some alien inscrutable outcome that just incidentally looks like optimizing for something intelligible for as long as the system remains sufficiently weak.)
anyway i’m still not very convinced of Doom despite this post’s argument against the emergence of internal optimization algorithms being apparently wrong, because i have doubts about whether efficient explicit utility maximizers are even possible, not to mention the question of whether the particular inducitve biases of deep learning would actually lead to them being discovered. but… the big flashy argument this post had for that conclusion got poofed.
anyway i’m still not very convinced of Doom [...], because i have doubts about whether efficient explicit utility maximizers are even possible,
What? I’m not sure what you mean be “efficient” utility maximizers, but I think you’re setting too high a bar for being concerned. I don’t think doom is certain but I think it’s obviously possible. Humans are dangerous, and we are possible. Anything smarter than humans is more dangerous if it has misaligned goals. We are building things that will become smarter than us. They will have goals. We do not know how to make those goals ones that are aligned with human goals. That is enough to be very concerned and want to work toward a safe future.
(We definitely have ideas about how to align AGI—see my work on instruction-following for both hopes and fears, and my work on system 2 alignment for technical approaches on the current path to LLM-based AGI. But this is all highly uncertain. Very optimistic takes leave out the hard parts of the problem.)
it seems unlikely to me that they’ll end up with like, strong, globally active goals in the manner of an expected utility maximizer, and it’s not clear to me that it’s likely for the goals they do develop to end up sufficiently misaligned as to cause a catastrophe. like… you get LLMs to situationally steer certain situations in certain directions by RLing it when it actually does steer those situations in those directions; if you do that enough, hopefully it catches the pattern. and… to the extent that it doesn’t catch the pattern, it’s not clear that it will instead steer those kinds of situations (let alone all situations) towards some catastrophic outcome. their misgeneralizations can just result in noise, or taking actions that steer certain situations into weird but ultimately harmless territory. it seems like the catastrophic outcomes are a very small subset of the ways this could end up going wrong, since you’re not giving them goals to pursue relentlessly, you’re just giving them feedback on the ways you want them to behave in particular types of situations.
Hm. I think you’re thinking of current LLMs, not AGI agents based on LLMs? If so I fully agree that they’re unlkely to be dangerous at all.
I’m worried about agentic cognitive architectures we’ve built with LLMs as the core cognitive engine. We are trying to make them goal directed and to have human-level competence; superhuman competence/intelligence follows after that if we don’t somehow halt progress permanently.
Current LLMs, like most humans most of the time, aren’t strongly goal directed. But we want them to be strongly goal-directed so they do the tasks we give them.
Doing a task with full competence is the same as maximizing that goal. Which would be fine if we can define those goals adequately, but we’re not at all sure we can as I emphasized last.
When you have a goal, pursuing it relentlessly is the default, not some weird special case. Evolution had to carefully balance our different goals with our homeostatic needs, and humans still often adopt strange goals and work toward them energetically (if they have time and money and until they die). And again, humans are dangerous as hell to other humans. Civilization is a sort of detente based on our individually having very limited capabilities so that we need to collaborate to succeed.
I’m not sure how you’re imagining that we have AI that can get really valuable stuff done and we don’t turn it into AGI that has goals because we wanted it to and designed it to pursue long-term goals so they can do real work. They’ll need to be able to solve solve new problems (like “how do I open this file if my first try fails” but general problem-solving extends to “how do I keep the humans from finding out”). That sounds intuitively super dangerous to me.
I agree that LLMs themselves aren’t likely to be dangerous no matter how smart they get. They’ll only be dangerous once we extend them to persistently pursue goals.
And we’re hard at work doing exactly that.
I don’t think this is very relevant, but even if we don’t give them persistent goals, LLM agents that can reflect and remember their conclusions are likely to come up with their own long-term goals—just like people do. I’m writing about that right now and will try to remember to link it here once it’s posted. But the more likely scenario is that they interpret the goals we give them differently than we’d hoped.
my view is that humans obtain their goals largely by a reinforcement learning process, and that they’re therefore good evidence about both how you can bootstrap up to goal-directed behavior via reinforcement learning, and the limitations of doing so. the basic picture is that humans pursue goals (e.g. me, trying to write the OP) largely as a byproduct of me reliably feeling rewarded during the process, and punished for deviating from that activity. like i enjoy writing and research, and also writing let me feel productive and therefore avoid thinking about some important irl things i’ve been needing to get done for weeks, and these dynamics can be explained basically in the vocabulary of reinforcement learning. this gives us a solid idea of how we’d go about getting similar goals into deep learning-based AGI.
(edit: also it’s notable that even when writing this post i was sometimes too frustrated, exhausted, or distracted by socialization or the internet to work on it, suggesting it wasn’t actually a 100% relentless goal of mine, and that goals in general don’t have to be that way.)
it’s also worth noting that getting humans to pursue goals consistently does require kind of meticulous reinforcement learning. like… you can kind of want to do your homework, but find it painful enough to do that you bounce back and forth between doing it and scrolling twitter. same goes for holding down a job or whatever. learning to reliably pursue objectives that foster stability is like, the central project of maturation, and the difficulty of it suggests the difficulty of getting an agent that relentlessly pursues some goal without the RL process being extremely encouraging of them moving along in that direction.
(one central advantage that humans have over natural selection wrt alignment is that we can much more intelligently evaluate which of an agent’s actions we want to reinforce. natural selection gave us some dumb, simple reinforcement triggers, like cuddles or food or sex, and has to bootstrap up to more complex triggers associatively over the course of a lifetime. but we can use a process like RLAIF to automate the act of intelligently evaluating which actions can be expected to further our actual aims, and reinforce those.)
anyway, in order for alignment via RL to go wrong, you need a story about how an agent specifically misgeneralizes from its training process to go off and pursue something catastrophic relative to your values, which… doesn’t seem like a super easy outcome to achieve given how reliably you need to reinforce something in order for it to stick as a goal the system ~relentlessly pursues? like surely with that much data, we can rely on deep learning’s obvious in practice tendency to generalize ~correctly...
I’m actually interested in your responses here. This is useful for my strategies how I frame things and understanding different people’s intuitions.
Do you think we can’t make autonomous agents that pursue goals well enough to get things done? Do you really think they’ll lose focus between being goal-focused long enough for useful work, and long enough for taking over the world if they interpret their goals differently than we intended? Do you think there’s no way RL or natural language could be misinterpreted?
I’m thinking it’s easy to keep an LLM agent goal-focused; if RL doesn’t do it, we’d just have a bit of scaffolding that every so often injects a prompt “remember, keep working on [goal]!”
The inference-compute scaling results seem to indicate that chain of thought RL already has o1 and o3 staying task focused for millions of tokens.
If you’re superintelligent/competent, it doesn’t take 100% focus to take over the world, just occasionally coming back to the project and not completely changing your mind.
Ghengis Khan probably got distracted a lot but he did alright at murdering, and he was only human.
Humans are optimizing AI and then AGI to get things done. If they can do that, we should ask what they’re going to want to do.
Deep learning typically generalizes correctly within the training set. Once something is superintelligent and unstoppable, we’re going to be way outside of the training set.
Humans change their goals all the time, when they reach new conclusions about how the world works and how that changes their interpretations of their previous goals.
I am curious about your intuitions but I’ve got to focus on work so that’s got to be my last object-level contribution. Thanks for conversing.
I also think it should be easy-ish to keep deep learning-based systems goal-focused, though mostly because I imagine that at some point, we’ll have agents which are actively undergoing more RL while they’re still in deployment. This means you can replicate the way humans learn to stay focused on tasks they’re passionate about by just being positively reinforced for doing it all the time. My contention is just that, to the extent that the RL is misunderstood, it probably won’t lead to a massive catastrophe. It’s hard to think about this in the absence of concrete scenarios, but… I think to get a catastrophe, you need the system to be RL’d in ways that reliably teach it behaviors that steer a given situation towards a catastrophic outcome? I don’t think you like, reliably reinforce the model for being nice to humans, but it misunderstands “being nice to humans” in such a way that causes it to end up steering the future towards some weird undesirable outcome; Claude does well enough at this kind of thing in practice.
I think a real catastrophe has to look something like… you pretrain a model to give it an understanding of the world, then you RL it to be really good at killing people so you can use it as a military weapon, but you don’t also RL it to be nice to people on your own side, and then it goes rogue and starts killing people on your own side. I guess that’s a kind of “misunderstanding your creators’ intentions”, but like… I expect those kinds of errors to follow from like, fairly tractable oversights in terms of teaching a model the right caveats to intended but dangerous behavior. I don’t think e.g. RLing Claude to give good advice to humans when asked could plausibly lead to it acquiring catastrophic values.
edit: actually, maybe a good reference point for this is when humans misunderstand their own reward functions? i.e. “i thought i would enjoy this but i didn’t”? i wonder if you could mitigate problems in this area just by telling an llm the principles used for its constitution. i need to think about this more...
I don’t have formal education on computer science, but my feeling is that the paper doesn’t falsify your idea.
I think that transformers are very good at learning a ton of different algorithms for ton of different tasks. E.g. they might learn to use the clock algorithm to do addition. They probably also learn algorithms to translate languages with different grammatical rules.
I think the paper just demonstrates that one of these algorithms emulates gradient descent (or a version of it), and that the AI uses this algorithm for linear regression problems.
But just because the AI is capable of running this gradient descent algorithm in a single pass, doesn’t mean all of the AI’s behaviours are gradient descent products of some mesa-optimizer goal (completely different than the reward function).
In fact, I still think most of the AI’s single pass behaviours are gradient descent products of the outer optimization loop (i.e. the reward function or pretraining), just like you predict.
But
Although I don’t think that study falsifies your argument, I’m still unsure about one part of your argument:
Modern AI do a long chain-of-thought to search for the best output. This consists of very many forward passes. Doesn’t this looks like a looping algorithm to you?
One analogy is that for a single neuron firing, its behaviour has only been optimized by the outer optimization loop (evolution). But the thoughts represented by many neuron firings is optimizing towards a mesa-optimizer goal like “eat delicious food,” without any regard towards evolutionary fitness.
Anyways
Thank you for sharing this, and thank you so much for being willing to admit mistake. That is very hard and you are probably much better at it than me haha.
See No convincing evidence for gradient descent in activation space
Yes, and relatedly LLMs are run in loops just to generate more than one token in general. This is different than running an explicit optimization algorithm within a single forward pass.
Anyway, the part of my post the paper falsifies is that it’s forbiddingly difficult for neural networks to implement explicit, internal optimization algorithms. I don’t think the paper is strong evidence that all of a trained transformer’s outputs are generated primarily/exclusively by means of such an algorithm, and running gradient descent with a predictive objective internally sounds a lot less dangerous than running a magically functional AIXI approximation internally anyway. So there are still major assumptions made by RFLO that haven’t born out in reality yet.
The fundamental idea about genes having an advantage over weights at internally implementing looping algorithms is apparently wrong though (even though I don’t understand how the contrary is possible...).
I’ve been trying to understand this myself. Here’s a the understanding I’ve come to, which is very simplistic. If someone who knows more about transformers than me says I’m wrong I will defer to them.
I used this paper to come to this understanding.
In order to have a mesa-optimizer, lots and lots of layers need to be in on the game of optimization, rather than just one or several key elements which gets referenced repeatedly during the optimization process.
But self-attention is, by default, not very far away from being one step in gradient descent. Every layer doesn’t need to learn to do optimization independently from scratch, since it’s relatively easy to find given the self-attention architecture.
That’s why it’s not forbiddingly difficult for neural networks to implement internal optimization algorithms. It still could be forbiddingly difficult for most optimization algorithms, ones that aren’t easy to find from the basic architecture.
if you have a more detailed grasp on how exactly self-attention is close to a gradient descent step please do let me know, i’m having a hard time making sense of the details of these papers
Note that if computing an optimization step reduces the loss, the training process will reinforce it, even if other layers aren’t doing similar steps, so this is another reason to expect more explicit optimizers.
Basically, self attention is a function of certain matrices, something like this:
ej←ej+∑hPhWh,V∑ieh,i⊗eh,iWTh,KWh,Qej
Which looks really messy when you put it like this but is pretty natural in context.
If you can get the big messy looking term to approximate a gradient descent step for a given loss function, then you’re golden.
In appendix A.1., they show the matrices that yield this gradient descent step. They are pretty simple, and probably an easy point of attraction to find.
All of this reasoning is pretty vague, and without the experimental evidence it wouldn’t be nearly good enough. So there’s definitely more to understand here. But given the experimental evidence I think this is the right story about what’s going on.
Why do you think genes don’t have an advantage over weights for mesaoptimization? The paper shows that weights can do it but mightn’t genes still have an advantage?
I didn’t follow the details, I’m just interested in the logic you’re applying to conclude that your theoretical work was invalidated by that empirical study.
I also think Yudkowsky’s argument isn’t just about mesaoptimizers. You can have the whole network optimize for the training set, and just be disappointed to find out that it didn’t learn what you’d hoped it would learn when it gets into new environments. If we imagined that evolution was a person named Evie, she’d think her training technique worked great. If she came back now and saw human population declining from birth control use and shifting cultural values, shed realize it didn’t work nearly as well as she’d thought from evidence in the ancestral environment because they were optizing for the sex part and not the reproduction part. Steve Byrnes as usual has a very lucid breakdown of this logic. That’s not mesaoptimization or inner misalignment, just alignment misgeneralization. I think any algorithm strong enough to optimize anything might be optimizing something different than it’s trainer hoped when they built the training environment.
Mesaoptimization is one way to get misalignment but not the only way.
So supposing that’s all roughly correct, what about the lesson to be learned in research or theory development? I think you’ve drawn the correct one, but in the wrong direction: Doing more lit review saves time and heartache, but reviewing different ways of viewing the question you’re trying to address with your theory is at least as important as reviewing for empirical evidence.
That was my conclusion after doing cognitive neuroscience theory for a long time, and observing others doing it. There was a tendency to put a bunch of work into developing and writing up your theory, then to make the last step before publication doing a thorough lit review to make sure you didn’t look like an idiot when you published. Or not doing that and having one of the reviewers say something like “ummm I don’t think your interpretation of that theory was what the author meant, so you’re fighting a straw man here...”
At that point, people would be so invested that they’d do intellectual backflips to twist the interpretation around to publish that work (they’d perish if they gave up a year of work very often). Sometimes it could be reframed in light of the evidence or better understanding of existing theory, sometimes it wound up being basically counterproductive since the incentive was to fool yourself, which required writing something convincing enough to fool a bunch of other people.
Thanks for not doing that! It is so good to be in a community with strong values of epistemic rigor and honesty with ones self and others. Many kudos to you for withdrawing the piece instead of pushing on—and then publishing it with the admission of what went wrong for others to learn from.
in the section of the post i didn’t finish and therefore didn’t include here, i talk about how like… okay so valuing some outcome is about reliably taking actions which increase the subjective probability of that outcome occurring. explicit utility maximizers are constantly doing this by nature, but systems that acquire their values via RL (such as humans and chat models) only do so contextually and imperfectly. like… the thing RL fundamentally is, is a way of learning to produce outputs that predictably get high reward from the loss function. this only creates systems which optimize over the external world to the extent that, in certain situations, the particular types of actions a model learns happen to tend to steer the future in particular directions. so… failures of generalization here don’t necessarily result in systems that optimize effectively for anything at all; their misgeneralized behavior can in principle just be noise, and indeed it typically empirically is in deep learning, e.g. memorizing the training data.
(see also the fact that e.g. claude sometimes steers the future from certain “tributary states” like the user asking it for advice towards certain attractor basisns, like the user making a good decision. claude does this reliably despite not trying to optimize the cosmos for something else besides that. and it’s hard to imagine concretely what a “distributional shift” that would cause asked-for-advice claude to start reliably giving bad advice; maybe if the user has inhuman psychology, i guess? such that claude’s normal advice was bad? idk. i suppose claude can be prompted to be a little bit malicious if you really know what you’re doing, which can “steer the world” towards mildly but still targetedly bad outcomes given certain input states...)
anyway, humans are examples of systems that do somewhat effectively optimize for things other than what they were trained to optimize for, but that’s an artifact of the particular methods natural selection bestowed upon us for maximizing inclusive genetic fitness (namely a specific effective RL-ish setup). in this post, i was trying to argue that certain classes of setups that do reliably produce that kind of outcome, such as a subset of explicit optimization algorithms, are unlikely under gradient descent. but apparently it’s just not actually that hard to build explicit optimization algorithms under gradient descent. so, poof goes the argument.
Did the paper really show that they’re explicit optimizers? If so, what’s your definition of them?
I have representations of future states and I choose actions that might lead toward them. Those representations of my goals are explicit, so I’d call myself an explicit optimizer.
I added a bunch to the previous comment in an edit, sorry! I was switching from phone to laptop when it got longer. So you might want to look at it again to see if you got the full version.
“explicit optimizer” here just means that you search through some space of possibilities, and eventually select one that scores high according to some explicit objective function. (this is also how MIRI’s RFLO paper defines optimization.) the paper strongly suggests that neural networks sometimes run something like gradient descent internally, which fits this definition. it’s not necessarily about scheming to reach long-term goals in the external world, though that’s definitely a type of optimization.
(it’s clear that Claude etc. can do that kind of optimization verbally, i.e. not actually within its own weights; it can think through multiple ideas for action, rank them, and pick the best one too. the relevant difference between this and paperclip-style optimization is that its motivation to actually pursue any given goal is dependent on its weights; you could totally prompt an LLM to with a natural language command to pursue some goal, but it refuses because it’s been trained to not pursue such goals. and this relates to the things where like… at the layer of natural language processing anyway, your verbally thought “goals” are more like attempts to steer a fuzzy inference process, which itself may or may not have an explicit internal representation of end-state it’s actually aiming at. if not, the yudkowsian image of utility maximization becomes misleading, and there’s no longer reason to expect the system to be “trying” to steer the system towards some alien inscrutable outcome that just incidentally looks like optimizing for something intelligible for as long as the system remains sufficiently weak.)
anyway i’m still not very convinced of Doom despite this post’s argument against the emergence of internal optimization algorithms being apparently wrong, because i have doubts about whether efficient explicit utility maximizers are even possible, not to mention the question of whether the particular inducitve biases of deep learning would actually lead to them being discovered. but… the big flashy argument this post had for that conclusion got poofed.
What? I’m not sure what you mean be “efficient” utility maximizers, but I think you’re setting too high a bar for being concerned. I don’t think doom is certain but I think it’s obviously possible. Humans are dangerous, and we are possible. Anything smarter than humans is more dangerous if it has misaligned goals. We are building things that will become smarter than us. They will have goals. We do not know how to make those goals ones that are aligned with human goals. That is enough to be very concerned and want to work toward a safe future.
(We definitely have ideas about how to align AGI—see my work on instruction-following for both hopes and fears, and my work on system 2 alignment for technical approaches on the current path to LLM-based AGI. But this is all highly uncertain. Very optimistic takes leave out the hard parts of the problem.)
it seems unlikely to me that they’ll end up with like, strong, globally active goals in the manner of an expected utility maximizer, and it’s not clear to me that it’s likely for the goals they do develop to end up sufficiently misaligned as to cause a catastrophe. like… you get LLMs to situationally steer certain situations in certain directions by RLing it when it actually does steer those situations in those directions; if you do that enough, hopefully it catches the pattern. and… to the extent that it doesn’t catch the pattern, it’s not clear that it will instead steer those kinds of situations (let alone all situations) towards some catastrophic outcome. their misgeneralizations can just result in noise, or taking actions that steer certain situations into weird but ultimately harmless territory. it seems like the catastrophic outcomes are a very small subset of the ways this could end up going wrong, since you’re not giving them goals to pursue relentlessly, you’re just giving them feedback on the ways you want them to behave in particular types of situations.
Hm. I think you’re thinking of current LLMs, not AGI agents based on LLMs? If so I fully agree that they’re unlkely to be dangerous at all.
I’m worried about agentic cognitive architectures we’ve built with LLMs as the core cognitive engine. We are trying to make them goal directed and to have human-level competence; superhuman competence/intelligence follows after that if we don’t somehow halt progress permanently.
Current LLMs, like most humans most of the time, aren’t strongly goal directed. But we want them to be strongly goal-directed so they do the tasks we give them.
Doing a task with full competence is the same as maximizing that goal. Which would be fine if we can define those goals adequately, but we’re not at all sure we can as I emphasized last.
When you have a goal, pursuing it relentlessly is the default, not some weird special case. Evolution had to carefully balance our different goals with our homeostatic needs, and humans still often adopt strange goals and work toward them energetically (if they have time and money and until they die). And again, humans are dangerous as hell to other humans. Civilization is a sort of detente based on our individually having very limited capabilities so that we need to collaborate to succeed.
WRT LLMs pursuing goals as though they’re maximizers, they do once they are given a goal to pursue. see the recent post on how RL runaway optimisation problems are still relevant with LLMs.
I’m not sure how you’re imagining that we have AI that can get really valuable stuff done and we don’t turn it into AGI that has goals because we wanted it to and designed it to pursue long-term goals so they can do real work. They’ll need to be able to solve solve new problems (like “how do I open this file if my first try fails” but general problem-solving extends to “how do I keep the humans from finding out”). That sounds intuitively super dangerous to me.
I agree that LLMs themselves aren’t likely to be dangerous no matter how smart they get. They’ll only be dangerous once we extend them to persistently pursue goals.
And we’re hard at work doing exactly that.
I don’t think this is very relevant, but even if we don’t give them persistent goals, LLM agents that can reflect and remember their conclusions are likely to come up with their own long-term goals—just like people do. I’m writing about that right now and will try to remember to link it here once it’s posted. But the more likely scenario is that they interpret the goals we give them differently than we’d hoped.
my view is that humans obtain their goals largely by a reinforcement learning process, and that they’re therefore good evidence about both how you can bootstrap up to goal-directed behavior via reinforcement learning, and the limitations of doing so. the basic picture is that humans pursue goals (e.g. me, trying to write the OP) largely as a byproduct of me reliably feeling rewarded during the process, and punished for deviating from that activity. like i enjoy writing and research, and also writing let me feel productive and therefore avoid thinking about some important irl things i’ve been needing to get done for weeks, and these dynamics can be explained basically in the vocabulary of reinforcement learning. this gives us a solid idea of how we’d go about getting similar goals into deep learning-based AGI.
(edit: also it’s notable that even when writing this post i was sometimes too frustrated, exhausted, or distracted by socialization or the internet to work on it, suggesting it wasn’t actually a 100% relentless goal of mine, and that goals in general don’t have to be that way.)
it’s also worth noting that getting humans to pursue goals consistently does require kind of meticulous reinforcement learning. like… you can kind of want to do your homework, but find it painful enough to do that you bounce back and forth between doing it and scrolling twitter. same goes for holding down a job or whatever. learning to reliably pursue objectives that foster stability is like, the central project of maturation, and the difficulty of it suggests the difficulty of getting an agent that relentlessly pursues some goal without the RL process being extremely encouraging of them moving along in that direction.
(one central advantage that humans have over natural selection wrt alignment is that we can much more intelligently evaluate which of an agent’s actions we want to reinforce. natural selection gave us some dumb, simple reinforcement triggers, like cuddles or food or sex, and has to bootstrap up to more complex triggers associatively over the course of a lifetime. but we can use a process like RLAIF to automate the act of intelligently evaluating which actions can be expected to further our actual aims, and reinforce those.)
anyway, in order for alignment via RL to go wrong, you need a story about how an agent specifically misgeneralizes from its training process to go off and pursue something catastrophic relative to your values, which… doesn’t seem like a super easy outcome to achieve given how reliably you need to reinforce something in order for it to stick as a goal the system ~relentlessly pursues? like surely with that much data, we can rely on deep learning’s obvious in practice tendency to generalize ~correctly...
I’m actually interested in your responses here. This is useful for my strategies how I frame things and understanding different people’s intuitions.
Do you think we can’t make autonomous agents that pursue goals well enough to get things done? Do you really think they’ll lose focus between being goal-focused long enough for useful work, and long enough for taking over the world if they interpret their goals differently than we intended? Do you think there’s no way RL or natural language could be misinterpreted?
I’m thinking it’s easy to keep an LLM agent goal-focused; if RL doesn’t do it, we’d just have a bit of scaffolding that every so often injects a prompt “remember, keep working on [goal]!”
The inference-compute scaling results seem to indicate that chain of thought RL already has o1 and o3 staying task focused for millions of tokens.
If you’re superintelligent/competent, it doesn’t take 100% focus to take over the world, just occasionally coming back to the project and not completely changing your mind.
Ghengis Khan probably got distracted a lot but he did alright at murdering, and he was only human.
Humans are optimizing AI and then AGI to get things done. If they can do that, we should ask what they’re going to want to do.
Deep learning typically generalizes correctly within the training set. Once something is superintelligent and unstoppable, we’re going to be way outside of the training set.
Humans change their goals all the time, when they reach new conclusions about how the world works and how that changes their interpretations of their previous goals.
I am curious about your intuitions but I’ve got to focus on work so that’s got to be my last object-level contribution. Thanks for conversing.
I also think it should be easy-ish to keep deep learning-based systems goal-focused, though mostly because I imagine that at some point, we’ll have agents which are actively undergoing more RL while they’re still in deployment. This means you can replicate the way humans learn to stay focused on tasks they’re passionate about by just being positively reinforced for doing it all the time. My contention is just that, to the extent that the RL is misunderstood, it probably won’t lead to a massive catastrophe. It’s hard to think about this in the absence of concrete scenarios, but… I think to get a catastrophe, you need the system to be RL’d in ways that reliably teach it behaviors that steer a given situation towards a catastrophic outcome? I don’t think you like, reliably reinforce the model for being nice to humans, but it misunderstands “being nice to humans” in such a way that causes it to end up steering the future towards some weird undesirable outcome; Claude does well enough at this kind of thing in practice.
I think a real catastrophe has to look something like… you pretrain a model to give it an understanding of the world, then you RL it to be really good at killing people so you can use it as a military weapon, but you don’t also RL it to be nice to people on your own side, and then it goes rogue and starts killing people on your own side. I guess that’s a kind of “misunderstanding your creators’ intentions”, but like… I expect those kinds of errors to follow from like, fairly tractable oversights in terms of teaching a model the right caveats to intended but dangerous behavior. I don’t think e.g. RLing Claude to give good advice to humans when asked could plausibly lead to it acquiring catastrophic values.
edit: actually, maybe a good reference point for this is when humans misunderstand their own reward functions? i.e. “i thought i would enjoy this but i didn’t”? i wonder if you could mitigate problems in this area just by telling an llm the principles used for its constitution. i need to think about this more...