I really wish more people skeptical of AI capabilities would read about scaling laws -- Lilian is always so marvelous at giving a deep overview of the technical side but the whole point of this is: there are scaling laws, and they hold and continue to hold. This is such a huge basis for the predictions about AI capabilities for the past like 5 years.
Why should the skeptics be reading it? The scaling laws show diminishing returns on more training data and larger models.
From the Kaplan scaling laws paper:
> We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized training computation Cmin, as encapsulated in Equations (1.5) and (1.6). Conversely, we find very weak dependence on many architectural and optimization hyperparameters. Since scalings with N,D,Cmin are power-laws, there are diminishing returns with increasing scale.
So the skeptics are right to be skeptical of LLMs being all you need for continued advancement in this space. It seems like the believers are the ones who need to learn about the scaling laws.
> We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized training computation Cmin, as encapsulated in Equations (1.5) and (1.6). Conversely, we find very weak dependence on many architectural and optimization hyperparameters. Since scalings with N,D,Cmin are power-laws, there are diminishing returns with increasing scale.
This is why, because this completely misses the point. Every power law has diminishing marginal returns, that is by construction. The point is that they diminish predictably and smoothly across seven orders of magnitude of compute. There is no plateau anywhere in the observed range, and in fact larger models more sample efficient than expected.
You can't just cherry pick one sentence containing the phrase "diminishing returns" and ignore the actual paper. The whole reason the labs spent the GDP of a small country on GPUs is that the curve is boring and reliable. No one just was like "oops we missed that line in the Kaplan paper, shit".
Lots of arguments as to why things may slow down or hit fundamental limits requiring a paradigm shift. But at several points in the past 5 years many major players have assumed this to be the case and have been continually proven wrong. Not to mention the plethora of socioeconomic horrors coming out of this Pandora's box, which I believe to be a large colocated B300 compute cluster somewhere in the midwest.
A point that would have been consistent with Kaplan was them finding alpha ~ 0.05 on compute means halving the reducible loss is a million times more expensive. But this was invalidated by Chinchilla who found it to be ~0.36 (100x more compute, not 1million). And loss has a non-linear relationship with capabilities, but roughly halving pretraining loss is ~doubling the METR task horizon. We also haven't factored in something incredibly important which is: algorithmic / architectural / training pipeline gains. These are substantial too!
When I first saw scaling laws in that deep speech experiment notebook, I didn’t believe it could be real. I was worried for months that we made a mistake, or that it only worked for that one dataset.
I started to believe it after we (Joel Hestness in particular) reproduced it in so many experiments in “scaling is predictable empirically”.
The OpenAI work replicated it in a completely different environment, and at that point I was sure it was real.
Sometimes people ask me why I was so surprised by it. Prior work like Banko and Brill and the unreasonable effectiveness of data argued for more data. ML theory had similar models for toy problems, eg coin flips.
At the time I thought deep learning was supposed to be complex. Speech and language datasets seemed much more complex than toy problems. Optimization of deep transformers was complex.
The idea that it was possible for the whole thing to be governed by a 3 term equation seemed too simple. The implication was that it was simple to manufacture intelligence.
Ten years later, I still think it is still the most interesting observation I have seen. We are still learning what it looks like to live in a world where it is possible to manufacture intelligence.
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[ 3.2 ms ] story [ 32.9 ms ] threadFrom the Kaplan scaling laws paper:
> We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized training computation Cmin, as encapsulated in Equations (1.5) and (1.6). Conversely, we find very weak dependence on many architectural and optimization hyperparameters. Since scalings with N,D,Cmin are power-laws, there are diminishing returns with increasing scale.
So the skeptics are right to be skeptical of LLMs being all you need for continued advancement in this space. It seems like the believers are the ones who need to learn about the scaling laws.
This is why, because this completely misses the point. Every power law has diminishing marginal returns, that is by construction. The point is that they diminish predictably and smoothly across seven orders of magnitude of compute. There is no plateau anywhere in the observed range, and in fact larger models more sample efficient than expected.
You can't just cherry pick one sentence containing the phrase "diminishing returns" and ignore the actual paper. The whole reason the labs spent the GDP of a small country on GPUs is that the curve is boring and reliable. No one just was like "oops we missed that line in the Kaplan paper, shit".
Lots of arguments as to why things may slow down or hit fundamental limits requiring a paradigm shift. But at several points in the past 5 years many major players have assumed this to be the case and have been continually proven wrong. Not to mention the plethora of socioeconomic horrors coming out of this Pandora's box, which I believe to be a large colocated B300 compute cluster somewhere in the midwest.
A point that would have been consistent with Kaplan was them finding alpha ~ 0.05 on compute means halving the reducible loss is a million times more expensive. But this was invalidated by Chinchilla who found it to be ~0.36 (100x more compute, not 1million). And loss has a non-linear relationship with capabilities, but roughly halving pretraining loss is ~doubling the METR task horizon. We also haven't factored in something incredibly important which is: algorithmic / architectural / training pipeline gains. These are substantial too!
I started to believe it after we (Joel Hestness in particular) reproduced it in so many experiments in “scaling is predictable empirically”.
The OpenAI work replicated it in a completely different environment, and at that point I was sure it was real.
Sometimes people ask me why I was so surprised by it. Prior work like Banko and Brill and the unreasonable effectiveness of data argued for more data. ML theory had similar models for toy problems, eg coin flips.
At the time I thought deep learning was supposed to be complex. Speech and language datasets seemed much more complex than toy problems. Optimization of deep transformers was complex.
The idea that it was possible for the whole thing to be governed by a 3 term equation seemed too simple. The implication was that it was simple to manufacture intelligence.
Ten years later, I still think it is still the most interesting observation I have seen. We are still learning what it looks like to live in a world where it is possible to manufacture intelligence.
https://aclanthology.org/anthology-files/anthology-files/pdf...