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Okay, but narrowly focusing on a "quantization-robust unlearning strategy" as per the abstract might be a red herring, if that strategy doesn't incidentally also address other ways to undo the unlearning.

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I think it's useful because many people consume quantized models (most models that fit in your laptop will be quantized and not because people want to uncensor or un-unlearn anything). If you're training a model it makes sense to make the unlearning at least robust to this very common procedure.

This reminds of this very interesting paper [1] that finds that it's fairly "easy" to uncensor a model (modify it's refusal thingy)

[1] https://www.reddit.com/r/LocalLLaMA/comments/1cerqd8/refusal...

Yeah, exactly this. You would really want to pursue orthogonal methods for robust unlearning, so that you can still use quantization to check that the other methods worked.

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