This got me thinking.
Diffusion can be seen as an optimization process if you squint.
Classifier-free guidance works great for diffusion.
Is there an equivalent for optimizers?
Either taking difference between two momentums. Or using negative examples of poor quality data?
This got me thinking.
Diffusion can be seen as an optimization process if you squint.
Classifier-free guidance works great for diffusion.
Is there an equivalent for optimizers?
Either taking difference between two momentums. Or using negative examples of poor quality data?
similar: multi-token prediction I like to think of as creating "more tasks per forward pass" implicitly increasing your batch size. not exactly 1:1 but you're now taking a loss over N x dataset_tokens
similar: multi-token prediction I like to think of as creating "more tasks per forward pass" implicitly increasing your batch size. not exactly 1:1 but you're now taking a loss over N x dataset_tokens
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