... The pretraining thesis, while logically impeccable—how is a model supposed to solve all possible trick questions without understanding, just guessing?—never struck me as convincing, an argument admitting neither confutation nor conviction. It feels too much like a magic trick: “here’s some information theory, here’s a human benchmark, here’s how we can encode all tasks as a sequence prediction problem, hey presto—Intelligence!” There are lots of algorithms which are Turing-complete or ‘universal’ in some sense; there are lots of algorithms like AIXI which solve AI in some theoretical sense (Schmidhuber & company have many of these cute algorithms such as ‘the fastest possible algorithm for all problems’, with the minor catch of some constant factors which require computers bigger than the universe).

Why think pretraining or sequence modeling is not another one of them? Sure, if the model got a low enough loss, it’d have to be intelligent, but how could you prove that would happen in practice? (Training char-RNNs was fun, but they hadn’t exactly revolutionized deep learning.) It might require more text than exists, countless petabytes of data for all of those subtle factors like logical reasoning to represent enough training signal, amidst all the noise and distractors, to train a model. Or maybe your models are too small to do more than absorb the simple surface-level signals, and you would have to scale them 100 orders of magnitude for it to work, because the scaling curves didn’t cooperate. Or maybe your models are fundamentally broken, and stuff like abstraction require an entirely different architecture to work at all, and whatever you do, your current models will saturate at poor performance. Or it’ll train, but it’ll spend all its time trying to improve the surface-level modeling, absorbing more and more literal data and facts without ever ascending to the higher planes of cognition as planned. Or…

But apparently, it would’ve worked fine. Even RNNs probably would’ve worked—Transformers are nice, but they seem mostly be about efficiency. (Training large RNNs is much more expensive, and doing BPTT over multiple nodes is much harder engineering-wise.) It just required more compute & data than anyone was willing to risk on it until a few true-believers were able to get their hands on a few million dollars of compute.

GPT-2-1.5b had a cross-entropy WebText validation loss of ~3.3. GPT-3 halved that loss to ~1.73. For a hypothetical GPT-4, if the scaling curve continues for another 3 orders or so of compute (100–1000×) before crossing over and hitting harder diminishing returns , the cross-entropy loss will drop to ~1.24

If GPT-3 gained so much meta-learning and world knowledge by dropping its absolute loss ~50% when starting from GPT-2’s level, what capabilities would another ~30% improvement over GPT-3 gain? (Cutting the loss that much would still not reach human-level, as far as I can tell. ) What would a drop to ≤1, perhaps using wider context windows or recurrency, gain?