GPT-6 is supposed to be using a much larger base model that just finished pretraining so the "dump another chunk of all written language" approach is still going strong.
Modern pretraining also consists of expensive human-led specialized task creation and grading loops. Synthetic generation and distillation from previous models is another input for training. I wonder how much new text contributes beyond keeping knowledge up-to-date.
Yes, but not in the “dump another chunk of all written language in the bucket and stir”-sense which is what bitter lesson became synonymous with.
That may not be the intent of the original article, but over the past few years that’s what the phrase turned into.
GPT-6 is supposed to be using a much larger base model that just finished pretraining so the "dump another chunk of all written language" approach is still going strong.
Modern pretraining also consists of expensive human-led specialized task creation and grading loops. Synthetic generation and distillation from previous models is another input for training. I wonder how much new text contributes beyond keeping knowledge up-to-date.
Where did they get a second copy of all books, written/audio/visual media, and the web?
Couch cushions?
The measure is to see if the results scale, not just the rumored attempts at building such a model, as o3 taught us.
Where are they getting new data from at this point? Didn't they already read the entire internet?
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The bitter lesson just means “compute scaling beats hand-tuned architectures in the long run”.
As GP said. More RLHF is in fact the bitter lesson.