The ELI5 of the paper is that most "unlearning" methods can be regarded as adding some delta `w` to the parameters of the network, but most of `w` just gets "rounded away" during quantization (i.e. `quantize(X+w) ~= quantize(X)`). Pretty clever idea as a lot of cited methods explicitly optimize/regularize to keep `w` small to avoid degrading evaluation accuracy.
To your point, it does put into question the idea of whether these methods can actually be considered truly "unlearning" from an information-theoretic perspective (or if it is the equivalent of e.g. just putting `if (false)` around the still latent knowledge)
It’s probably more. Pretty conservatively, if the KV embedding dimension for each token is ~10K x 100 attention layers (this is roughly the scale of Llama3.1 405B) that’s already 1M 16-bit floats per token = 2MB. They have likely needed to implement some kind of KV compression (like DeepSeek) to make this even feasible.
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