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LLM scaling laws are pretty well established at this point. They probably won’t hold forever but we aren’t at the breaking point yet.

Some more pressing questions are:

* What new capabilities emerge as the models get better and better at predicting (i.e. loss goes down)?

* How much will it cost to train increasingly large models? And to run inference on them?

* How difficult will it be to find or generate more and more high quality data?




> LLM scaling laws are pretty well established at this point

what are they then? I thought everyone was firmly in the "let's train with more data and see what happens" camp




Scaling laws in terms of loss are well established.

How loss translates into higher-level capabilities is anyone's guess.




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