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Are LLMs at a practical limit for layer stacking? [D] (alien.top)

submitted 10 months ago by cstein123@alien.top to c/machinelearning@academy.garden

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Can LLMs stack more layers than the largest ones currently have, or is it bottlenecked? Is it because the gradients can’t propagate properly to the beginning of the network? Because inference would be to slow?

If anyone could provide a paper that talks about layer stacking scaling I would love to read it!

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[–] Brudaks@alien.top 1 points 10 months ago

The bottleneck is the total compute budget devoted to training, so while I'm quite certain that stacking a few more layers can be done and would have some benefit, it might well be that spending the same extra compute on a larger context window or 'wider' layers or simply doing more iterations on the same data would have a larger benefit than more layers, and if the people training the very large models think so, they would do these other things instead of stacking more layers.