The counter argument to above is that none of these models were really trained for multiple-epochs: it's hard to overfit data you've only seen once. But to go to 70T tokens, you'd inevitably have to start using many epochs.
The counter argument to above is that none of these models were really trained for multiple-epochs: it's hard to overfit data you've only seen once. But to go to 70T tokens, you'd inevitably have to start using many epochs.
With a large enough model and many epochs, you can certainly get overfitting, but for one epoch val/train curves look exactly the same and I'd expect that a 7B model will never overfit on 2T tokens no matter how many epochs you do.
Is this still true given that they're upsampling in the pretraining dataset? I don't recall any details on how and to what extent they did this in the Llama2 paper but presumably some fraction of those 2T training tokens is repeated data.
MetaAI hasn't been as averse to repeated tokens as other groups, they trained the now forgotten about Galactica for multiple epochs with good results.
> The validation curves would be considerably more convincing.
What are they validating on? I was under the impression they weren't splitting the pretraining corpus.
My guess is Llama2 upsamples Wikipedia a good bit, but given they didn’t report any information about training data, it’s hard to say.
Using a large & diverse training set is the best regulariser, but I think there is also weight decay and dropout in transformers