John Maxwell

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Why GPT wants to mesa-optimize & how we might change this

Your philosophical point is interesting; I have a post in the queue about that. However I don't think it really proves what you want it to.

Having John_Maxwell in the byline makes it far more likely that I'm the author of the post.

If humans can make useful judgements re: whether this is something I wrote, vs something nostalgebraist wrote to make a point about bylines, I don't see why a language model can't do the same, in principle.

GPT is trying to be optimal at next-step prediction, and an optimal next-step predictor should not get improved by lookahead, it should already have those facts priced in to its next-step prediction.

A perfectly optimal next-step predictor would not be improved by lookahead or anything else, it's perfectly optimal. I'm talking about computational structures which might be incentivized during training when the predictor is suboptimal. (It's still going to be suboptimal after training with current technology, of course.)

In orthonormal's post they wrote:

...GPT-3's ability to write fiction is impressive- unlike GPT-2, it doesn't lose track of the plot, it has sensible things happen, it just can't plan its way to a satisfying resolution.

I'd be somewhat surprised if GPT-4 shared that last problem.

I suspect that either GPT-4 will still be unable to plan its way to a satisfying resolution, or GPT-4 will develop some kind of internal lookahead (probably not beam search, but beam search could be a useful model for understanding it) which is sufficiently general to be re-used across many different writing tasks. (Generality takes fewer parameters.) I don't know what the relative likelihoods of those possibilities are. But the whole idea of AI safety is to ask what happens if we succeed.

Why GPT wants to mesa-optimize & how we might change this

So a predictor which seems (and is) frighteningly powerful at some short range L will do little better than random guessing if you chain its predictions up to some small multiple of L.

A system which develops small-L lookahead (for L > 1) may find large-L lookahead to be nearby in programspace. If so, incentivizing the development of small-L lookahead makes it more likely that the system will try large-L lookahead and find it to be useful as well (in predicting chess moves for instance).

My intuition is that small-L lookahead could be close to large-L lookahead in programspace for something like an RNN, but not for GPT-3's transformer architecture.

Anyway, the question here isn't whether lookahead will be perfectly accurate, but whether the post-lookahead distribution of next words will allow for improvement over the pre-lookahead distribution. Lookahead is almost certainly going to do better than random guessing, even topic models can do that.

By construction, language modeling gives you nothing to work with except the text itself, so you don't know who produced it or for whom.

Are you saying that GPT-3's training corpus was preprocessed to remove information about the author, title, and publication venue? Or are you only talking about what happens when this info is outside the context window?

Why GPT wants to mesa-optimize & how we might change this
  1. Stopping mesa-optimizing completely seems mad hard.

As I mentioned in the post, I don't think this is a binary, and stopping mesa-optimization "incompletely" seems pretty useful. I also have a lot of ideas about how to stop it, so it doesn't seem mad hard to me.

  1. Managing "incentives" is the best way to deal with this stuff, and will probably scale to something like 1,000,000x human intelligence.

I'm less optimistic about this approach.

  1. There is a stochastic aspect to training ML models, so it's not enough to say "the incentives favor Mesa-Optimizing for X over Mesa-Optimizing for Y". If Mesa-Optimizing for Y is nearby in model-space, we're liable to stumble across it.

  2. Even if your mesa-optimizer is aligned, if it doesn't have a way to stop mesa-optimization, there's the possibility that your mesa-optimizer would develop another mesa-optimizer inside itself which isn't necessarily aligned.

  3. I'm picturing value learning via (un)supervised learning, and I don't see an easy way to control the incentives of any mesa-optimizer that develops in the context of (un)supervised learning. (Curious to hear about your ideas though.)

My intuition is that the distance between Mesa-Optimizing for X and Mesa-Optimizing for Y is likely to be smaller than the distance between an Incompetent Mesa-Optimizer and a Competent Mesa-Optimizer. If you're shooting for a Competent Human Values Mesa-Optimizer, it would be easy to stumble across a Competent Not Quite Human Values Mesa-Optimizer along the way. All it would take would be having the "Competent" part in place before the "Human Values" part. And running a Competent Not Quite Human Values Mesa-Optimizer during training is likely to be dangerous.

On the other hand, if we have methods for detecting mesa-optimization or starving it of compute that work reasonably well, we're liable to stumble across an Incompetent Mesa-Optimizer and run it a few times, but it's less likely that we'll hit the smaller target of a Competent Mesa-Optimizer.

Why GPT wants to mesa-optimize & how we might change this

Now it's true that efficiently estimating that conditional using a single forward pass of a transformer might involve approximations to beam search sometimes.

Yeah, that's the possibility the post explores.

At a high level, I don't think we really need to be concerned with this form of "internal lookahead" unless/until it starts to incorporate mechanisms outside of the intended software environment (e.g. the hardware, humans, the external (non-virtual) world).

Is there an easy way to detect if it's started doing that / tell it to restrict its lookahead to particular domains? If not, it may be easier to just prevent it from mesa-optimizing in the first place. (The post has arguments for why that's (a) possible and (b) wouldn't necessarily involve a big performance penalty.)

Developmental Stages of GPTs

BTW with regard to "studying mesa-optimization in the context of such systems", I just published this post: Why GPT wants to mesa-optimize & how we might change this.

I'm still thinking about the point you made in the other subthread about MAML. It seems very plausible to me that GPT is doing MAML type stuff. I'm still thinking about if/how that could result in dangerous mesa-optimization.

Why GPT wants to mesa-optimize & how we might change this

Well I suppose mesa-optimization isn't really a binary is it? Like, maybe there's a trivial sense in which self-attention "mesa-optimizes" over its input when figuring out what to pay attention to.

But ultimately, what matters isn't the definition of the term "mesa-optimization", it's the risk of spontaneous internal planning/optimization that generalizes in unexpected ways or operates in unexpected domains. At least in my mind. So the question is whether this considering multiple possibilities about text stuff could also improve its ability to consider multiple possibilities in other domains. Which depends on whether the implementation of "considering multiple possibilities" looks more like beam search vs very domain-adapted heuristics.

Why GPT wants to mesa-optimize & how we might change this

This post distinguishes between mesa-optimization and learned heuristics. What you're describing sounds like learned heuristics. ("Learning which words are easy to rhyme" was an example I gave in the post.) Learned heuristics aren't nearly as worrisome as mesa-optimization because they're harder to modify and misuse to do planning in unexpected domains. When I say "lookahead" in the post I'm pretty much always referring to the mesa-optimization sort.

Developmental Stages of GPTs

The outer optimizer is the more obvious thing: it's straightforward to say there's a big difference in dealing with a superhuman Oracle AI with only the goal of answering each question accurately, versus one whose goals are only slightly different from that in some way.

GPT generates text by repeatedly picking whatever word seems highest probability given all the words that came before. So if its notion of "highest probability" is almost, but not quite, answering every question accurately, I would expect a system which usually answers questions accurately but sometimes answers them inaccurately. That doesn't sound very scary?

Developmental Stages of GPTs

esp. since GPT-3's 0-shot learning looks like mesa-optimization

Could you provide more details on this?

Sometimes people will give GPT-3 a prompt with some examples of inputs along with the sorts of responses they'd like to see from GPT-3 in response to those inputs ("few-shot learning", right? I don't know what 0-shot learning you're referring to.) Is your claim that GPT-3 succeeds at this sort of task by doing something akin to training a model internally?

If that's what you're saying... That seems unlikely to me. GPT-3 is essentially a stack of 96 transformers right? So if it was doing something like gradient descent internally, how many consecutive iterations would it be capable of doing? It seems more likely to me that GPT-3 is simply able to learn sufficiently rich internal representations such that when the input/output examples are within its context window, it picks up their input/output structure and forms a sufficiently sophisticated conception of that structure that the word that scores highest according to next-word prediction is a word that comports with the structure.

96 transformers would appear to offer a very limited budget for any kind of serial computation, but there's a lot of parallel computation going on there, and there are non-gradient-descent optimization algorithms, genetic algorithms say, that can be parallelized. I guess the query matrix could be used to implement some kind of fitness function? It would be interesting to try some kind of layer-wise pretraining on transformer blocks and train them to compute steps in a parallelizable optimization algorithm (probably you'd want to pick a deterministic algorithm which is parallelizable instead of a stochastic algorithm like genetic algorithms). Then you could look at the resulting network and based on it, try to figure out what the telltale signs of a mesa-optimizer are (since this network is almost certainly implementing a mesa-optimizer).

Still, my impression is you need 1000+ generations to get interesting results with genetic algorithms, which seems like a lot of serial computation relative to GPT-3's budget...

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