Comment by anon291
2 years ago
I'm not surprised you can't use it to make it better, but one might imagine gradients would go to zero as you fed the model its own output.
2 years ago
I'm not surprised you can't use it to make it better, but one might imagine gradients would go to zero as you fed the model its own output.
No, not even close. Gradients don't come to zero in the first place. Training is never perfect.
Let's restate. I'd imagine you end up in local minima that are difficult to escape using model generated data. So sure, non-zero gradients, but if you plot the gradients, I would expect them to orbit at that point. But it seems like they diverge.
Mini-batches and dropout mean that you are constantly jumping out of and into other minima during training of any type (highly-redundant solution space is an important feature of deep learning). This is deliberate and necessary to explore the gigantic parameter space of these huge LLM models.
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