Post
2613
๐ฐโ ๐๐๐ฌ๐๐๐ซ๐๐ก ๐๐จ๐ซ ๐ญ๐ก๐ ๐ฏ๐๐ซ๐ฒ ๐๐๐ ๐๐จ๐จ๐ซ - ๐๐๐๐ฅ๐ข๐ง๐ ๐ฅ๐๐ฐ๐ฌ ๐ซ๐๐ฉ๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง
๐ Good news: ๐๐ผ๐ ๐ฐ๐ฎ๐ป ๐ฑ๐ผ ๐ฐ๐๐๐๐ถ๐ป๐ด-๐ฒ๐ฑ๐ด๐ฒ ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐๐ถ๐๐ต ๐ฎ ๐ฐ๐ฎ๐น๐ฐ๐๐น๐ฎ๐๐ผ๐ฟ ๐ฎ๐ป๐ฑ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ฃ๐ฎ๐ถ๐ป๐ ๐ฎ๐ฌ๐ฌ๐ฒ!
The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to ๐ณ๐ถ๐ป๐ฑ ๐๐ต๐ฒ ๐ผ๐ฝ๐๐ถ๐บ๐ฎ๐น ๐ฟ๐ฎ๐๐ถ๐ผ ๐ผ๐ณ ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ถ๐๐ฒ ๐๐ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐๐ผ๐ธ๐ฒ๐ป๐. Why is this question so important?
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.
The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.
But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.
๐ฅ They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.
โก๏ธ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.
โ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!
๐ Good news: ๐๐ผ๐ ๐ฐ๐ฎ๐ป ๐ฑ๐ผ ๐ฐ๐๐๐๐ถ๐ป๐ด-๐ฒ๐ฑ๐ด๐ฒ ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐๐ถ๐๐ต ๐ฎ ๐ฐ๐ฎ๐น๐ฐ๐๐น๐ฎ๐๐ผ๐ฟ ๐ฎ๐ป๐ฑ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐ฃ๐ฎ๐ถ๐ป๐ ๐ฎ๐ฌ๐ฌ๐ฒ!
The Chinchilla experiments (by Google DeepMind) ran hundreds of pre-trainings with models >1B parameters (I do not want to imagine how much that cost) to ๐ณ๐ถ๐ป๐ฑ ๐๐ต๐ฒ ๐ผ๐ฝ๐๐ถ๐บ๐ฎ๐น ๐ฟ๐ฎ๐๐ถ๐ผ ๐ผ๐ณ ๐บ๐ผ๐ฑ๐ฒ๐น ๐๐ถ๐๐ฒ ๐๐ ๐๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐๐ผ๐ธ๐ฒ๐ป๐. Why is this question so important?
Well, you only ever have access to a fixed compute, counted in FLOPs (floating point operations). So if your model is bigger, you will have less compute to train on many tokens, and if you want to train on more tokens, your model will be smaller. When model trainings cost million, you absolutely need to get this right.
The new paper "Chinchilla Scaling: A replication attempt" by Epoch AI sets on on the ambitious goal of reproducing this.
But since the authors do not have infinite money, they decided to directly run their computations from DeepMind's own experiments! They took the figure from the last experiment (cf slide below), measured point positions, picked color codes, and ended up reconstructing the underlying data.
๐ฅ They then just fit the scaling laws proposed by the Chinchilla Authors, but arrived at wildly different results! They find that as a rough rule of thumb, you should use 20 training tokens for each parameter in your model, instead of the 70 obtained in the original paper. They also point out inconsistencies in the paper, and unrealistically narrow confidence intervals.
โก๏ธ This only contradicts the results from the last (out of 3) experiments in the Chinchilla paper. And the model trained at the end of the Chinchilla paper still seems properly scaled.
โ But it does show that a tiny bit more theoretical work can go a long way, especially given the huge financial costs that such an error can have!