One possible answer is that we are in what one might call an “unhobbling overhang.”
Aschenbrenner uses the term “unhobbling” for changes that make existing model capabilities possible (or easier) for users to reliably access in practice.
His presentation emphasizes the role of unhobbling as yet another factor growing the stock of (practically accessible) capabilities over time. IIUC, he argues that better/bigger pretraining would produce such growth (to some extent) even without more work on unhobbling, but in fact we’re also getting better at unhobbling over time, which leads to even more growth.
That is, Aschenbrenner treats pretraining improvements and unhobbling as economic substitutes: you can improve “practically accessible capabilities” to the same extent by doing more of either one even in the absence of the other, and if you do both at once that’s even better.
However, one could also view the pair more like economic complements. Under this view, when you pretrain a model at “the next tier up,” you also need to do novel unhobbling research to “bring out” the new capabilities unlocked at that tier. If you only scale up, while re-using the unhobbling tech of yesteryear, most of the new capabilities will be hidden/inaccessible, and this will look to you like diminishing downstream returns to pretraining investment, even though the model is really getting smarter under the hood.
This could be true if, for instance, the new capabilities are fundamentally different in some way that older unhobbling techniques were not planned to reckon with. Which seems plausible IMO: if all you have is GPT-2 (much less GPT-1, or char-rnn, or...), you’re not going to invest a lot of effort into letting the model “use a computer” or combine modalities or do long-form reasoning or even be an HHH chatbot, because the model is kind of obviously too dumb to do these things usefully no matter how much help you give it.
(Relatedly, one could argue that fundamentally better capabilities tend to go hand in hand with tasks that operate on a longer horizon and involve richer interaction with the real world, and that this almost inevitably causes the appearance of “diminishing returns” in the interval between creating a model smart enough to perform some newly long/rich task and the point where the model has actually been tuned and scaffolded to do the task. If your new model is finally smart enough to “use a computer” via a screenshot/mouseclick interface, it’s probably also great at short/narrow tasks like NLI or whatever, but the benchmarks for those tasks were already maxed out by the last generation so you’re not going to see a measurable jump in anything until you build out “computer use” as a new feature.)
This puts a different spin on the two concurrent observations that (a) “frontier companies report ‘diminishing returns’ from pretraining” and (b) “frontier labs are investing in stuff like o1 and computer use.”
Under the “unhobbling is a substitute” view, (b) likely reflects an attempt to find something new to “patch the hole” introduced by (a).
But under the “unhobbling is a complement” view, (a) is instead simply a reflection of the fact that (b) is currently a work in progress: unhobbling is the limiting bottleneck right now, not pretraining compute, and the frontier labs are intensively working on removing this bottleneck so that their latest pretrained models can really shine.
(On an anecdotal/vibes level, this also agrees with my own experience when interacting with frontier LLMs. When I can’t get something done, these days I usually feel like the limiting factor is not the model’s “intelligence” – at least not only that, and not that in an obvious or uncomplicated way – but rather that I am running up against the limitations of the HHH assistant paradigm; the model feels intuitively smarter in principle than “the character it’s playing” is allowed to be in practice. See my comments here and here.)
One possible answer is that we are in what one might call an “unhobbling overhang.”
Aschenbrenner uses the term “unhobbling” for changes that make existing model capabilities possible (or easier) for users to reliably access in practice.
His presentation emphasizes the role of unhobbling as yet another factor growing the stock of (practically accessible) capabilities over time. IIUC, he argues that better/bigger pretraining would produce such growth (to some extent) even without more work on unhobbling, but in fact we’re also getting better at unhobbling over time, which leads to even more growth.
That is, Aschenbrenner treats pretraining improvements and unhobbling as economic substitutes: you can improve “practically accessible capabilities” to the same extent by doing more of either one even in the absence of the other, and if you do both at once that’s even better.
However, one could also view the pair more like economic complements. Under this view, when you pretrain a model at “the next tier up,” you also need to do novel unhobbling research to “bring out” the new capabilities unlocked at that tier. If you only scale up, while re-using the unhobbling tech of yesteryear, most of the new capabilities will be hidden/inaccessible, and this will look to you like diminishing downstream returns to pretraining investment, even though the model is really getting smarter under the hood.
This could be true if, for instance, the new capabilities are fundamentally different in some way that older unhobbling techniques were not planned to reckon with. Which seems plausible IMO: if all you have is GPT-2 (much less GPT-1, or char-rnn, or...), you’re not going to invest a lot of effort into letting the model “use a computer” or combine modalities or do long-form reasoning or even be an HHH chatbot, because the model is kind of obviously too dumb to do these things usefully no matter how much help you give it.
(Relatedly, one could argue that fundamentally better capabilities tend to go hand in hand with tasks that operate on a longer horizon and involve richer interaction with the real world, and that this almost inevitably causes the appearance of “diminishing returns” in the interval between creating a model smart enough to perform some newly long/rich task and the point where the model has actually been tuned and scaffolded to do the task. If your new model is finally smart enough to “use a computer” via a screenshot/mouseclick interface, it’s probably also great at short/narrow tasks like NLI or whatever, but the benchmarks for those tasks were already maxed out by the last generation so you’re not going to see a measurable jump in anything until you build out “computer use” as a new feature.)
This puts a different spin on the two concurrent observations that (a) “frontier companies report ‘diminishing returns’ from pretraining” and (b) “frontier labs are investing in stuff like o1 and computer use.”
Under the “unhobbling is a substitute” view, (b) likely reflects an attempt to find something new to “patch the hole” introduced by (a).
But under the “unhobbling is a complement” view, (a) is instead simply a reflection of the fact that (b) is currently a work in progress: unhobbling is the limiting bottleneck right now, not pretraining compute, and the frontier labs are intensively working on removing this bottleneck so that their latest pretrained models can really shine.
(On an anecdotal/vibes level, this also agrees with my own experience when interacting with frontier LLMs. When I can’t get something done, these days I usually feel like the limiting factor is not the model’s “intelligence” – at least not only that, and not that in an obvious or uncomplicated way – but rather that I am running up against the limitations of the HHH assistant paradigm; the model feels intuitively smarter in principle than “the character it’s playing” is allowed to be in practice. See my comments here and here.)