Over the last 3 years we made a lot of progress in efficient training methods. On text models, you can now train GPT-2 levels of coherent English from scratch for about $50 (e.g. using Karpathy's nanochat [1])

1: https://github.com/karpathy/nanochat

That's insane. How does that work? Does it take so much less data now, or smaller models, or what? Apparently GPT-2 cost ~$40,000 to train when it came out.

I'm sure Moore's Law for GPUs helped a little, but I'm really curious about the savings on the methodology side. (The repo mentions "hyperparameters")