I don’t necessarily expect GPT-4 to do better on perplexity than would be predicted by a linear model fit to neuron count plus algorithmic progress over time; my guess for why they’re not scaling it bigger would be that Stack More Layers just basically stopped scaling in real output quality at the GPT-3 level. They can afford to scale up an OOM to 1.75 trillion weights, easily, given their funding, so if they’re not doing that, an obvious guess is that it’s because they’re not getting a big win from that. As for their ability to then make algorithmic progress, depends on how good their researchers are, I expect; most algorithmic tricks you try in ML won’t work, but maybe they’ve got enough people trying things to find some? But it’s hard to outpace a field that way without supergeniuses, and the modern world has forgotten how to rear those.
While GPT-4 wouldn’t be a lot bigger than GPT-3, Sam Altman did indicate that it’d use a lot more compute. That’s consistent with Stack More Layers still working; they might just have found an even better use for compute.
(The increased compute-usage also makes me think that a Paul-esque view would allow for GPT-4 to be a lot more impressive than GPT-3, beyond just modest algorithmic improvements.)
I believe Sam Altman implied they’re simply training a GPT-3-variant for significantly longer for “GPT-4”. The GPT-3 model in prod is nowhere near converged on its training data.
Edit: changed to be less certain, pretty sure this follows from public comments by Sam, but he has not said this exactly
Say more about the source for this claim? I’m pretty sure he didn’t say that during the Q&A I’m sourcing my info from. And my impression is that they’re doing something more than this, both on priors (scaling laws says that optimal compute usage means you shouldn’t train to convergence — why would they start now?) and based on what he said during that Q&A.
GPT-3 not being trained on even one pass of its training dataset
“Use way more compute” achieving outsized gains by training longer than by most other architectural modifications for a fixed model size (while you’re correct that bigger model = faster training, you’re trading off against ease of deployment, and models much bigger than GPT-3 become increasingly difficult to serve at prod. Plus, we know it’s about the same size, from the Q&A)
Some experience with undertrained enormous language models underperforming relative to expectation
This is not to say that GPT-4 wont have architectural changes. Sam mentioned a longer context at the least. But these sorts of architectural changes probably qualify as “small” in the parlance of the above conversation.
To be clear: Do you remember Sam Altman saying that “they’re simply training a GPT-3-variant for significantly longer”, or is that an inference from ~”it will use a lot more compute” and ~”it will not be much bigger”?
Because if you remember him saying that, then that contradicts my memory (and, uh, the notes that people took that I remember reading), and I’m confused.
While if it’s an inference: sure, that’s a non-crazy guess, and I take your point that smaller models are easier to deploy. I just want it to be flagged as a claimed deduction, not as a remembered statement.
(And I maintain my impression that something more is going on; especially since I remember Sam generally talking about how models might use more test-time compute in the future, and be able to think for longer on harder questions.)
One way they could do that, is by pitting the model against modified versions of itself, like they did in OpenAI Five (for Dota).
From the minimizing-X-risk perspective, it might be the worst possible way to train AIs.
As Jeff Clune (Uber AI) put it:
[O]ne can imagine that some ways of configuring AI-GAs (i.e. ways of incentivizing progress) that would make AI-GAs more likely to succeed in producing general AI also make their value systems more dangerous. For example, some researchers might try to replicate a basic principle of Darwinian evolution: that it is ‘red in tooth and claw.’
If a researcher tried to catalyze the creation of an AI-GA by creating conditions similar to those on Earth, the results might be similar. We might thus produce an AI with human vices, such as violence, hatred, jealousy, deception, cunning, or worse, simply because those attributes make an AI more likely to survive and succeed in a particular type of competitive simulated world. Note that one might create such an unsavory AI unintentionally by not realizing that the incentive structure they defined encourages such behavior.
Additionally, if you train a language model to outsmart millions of increasingly more intelligent copies of itself, you might end up with the perfect AI-box escape artist.
I don’t necessarily expect GPT-4 to do better on perplexity than would be predicted by a linear model fit to neuron count plus algorithmic progress over time; my guess for why they’re not scaling it bigger would be that Stack More Layers just basically stopped scaling in real output quality at the GPT-3 level. They can afford to scale up an OOM to 1.75 trillion weights, easily, given their funding, so if they’re not doing that, an obvious guess is that it’s because they’re not getting a big win from that. As for their ability to then make algorithmic progress, depends on how good their researchers are, I expect; most algorithmic tricks you try in ML won’t work, but maybe they’ve got enough people trying things to find some? But it’s hard to outpace a field that way without supergeniuses, and the modern world has forgotten how to rear those.
While GPT-4 wouldn’t be a lot bigger than GPT-3, Sam Altman did indicate that it’d use a lot more compute. That’s consistent with Stack More Layers still working; they might just have found an even better use for compute.
(The increased compute-usage also makes me think that a Paul-esque view would allow for GPT-4 to be a lot more impressive than GPT-3, beyond just modest algorithmic improvements.)
If they’ve found some way to put a lot more compute into GPT-4 without making the model bigger, that’s a very different—and unnerving—development.
I believe Sam Altman implied they’re simply training a GPT-3-variant for significantly longer for “GPT-4”. The GPT-3 model in prod is nowhere near converged on its training data.
Edit: changed to be less certain, pretty sure this follows from public comments by Sam, but he has not said this exactly
Say more about the source for this claim? I’m pretty sure he didn’t say that during the Q&A I’m sourcing my info from. And my impression is that they’re doing something more than this, both on priors (scaling laws says that optimal compute usage means you shouldn’t train to convergence — why would they start now?) and based on what he said during that Q&A.
This is based on:
The Q&A you mention
GPT-3 not being trained on even one pass of its training dataset
“Use way more compute” achieving outsized gains by training longer than by most other architectural modifications for a fixed model size (while you’re correct that bigger model = faster training, you’re trading off against ease of deployment, and models much bigger than GPT-3 become increasingly difficult to serve at prod. Plus, we know it’s about the same size, from the Q&A)
Some experience with undertrained enormous language models underperforming relative to expectation
This is not to say that GPT-4 wont have architectural changes. Sam mentioned a longer context at the least. But these sorts of architectural changes probably qualify as “small” in the parlance of the above conversation.
To be clear: Do you remember Sam Altman saying that “they’re simply training a GPT-3-variant for significantly longer”, or is that an inference from ~”it will use a lot more compute” and ~”it will not be much bigger”?
Because if you remember him saying that, then that contradicts my memory (and, uh, the notes that people took that I remember reading), and I’m confused.
While if it’s an inference: sure, that’s a non-crazy guess, and I take your point that smaller models are easier to deploy. I just want it to be flagged as a claimed deduction, not as a remembered statement.
(And I maintain my impression that something more is going on; especially since I remember Sam generally talking about how models might use more test-time compute in the future, and be able to think for longer on harder questions.)
Honestly, at this point, I don’t remember if it’s inferred or primary-sourced. Edited the above for clarity.
One way they could do that, is by pitting the model against modified versions of itself, like they did in OpenAI Five (for Dota).
From the minimizing-X-risk perspective, it might be the worst possible way to train AIs.
As Jeff Clune (Uber AI) put it:
Additionally, if you train a language model to outsmart millions of increasingly more intelligent copies of itself, you might end up with the perfect AI-box escape artist.