The problem with using softmax is that it forces each attention head to make an annotation, even if it has no information to add to the output vector. Using softmax to choose among discrete alternatives is great; using it for optional annotation (i.e. as input into addition) is, like, not cool, man.

I'm saying it uses the current position to do this, that if it was a significant error I would expect it to improve the training loss. I sort of interpreted the blog post as being a bit more positive on the idea than just being about improving the quantization