I don't think that's what they're saying at all. They're talking not about qualia in the human sense, but specifically about "the qualia of their own training". That is, the corpus that LLMs "learn" from and the "experiences" of those texts that are generalized during the training process. Both the raw data and the memory of "learning" is discarded.

So if one were to improve an LLM along those lines, I believe it would be something like: 1) LLM is asked a question. 2) LLM comes up with an initial response. 3) LLM retrieves the related "learning" history behind that answer and related portions of the corpus. 4) LLM compares the initial answer with the richer set of information, looking for conflicts between the initial answer and the broader set, or "learning" choices that may be false. 6) LLM generates a better answer and gives it. 7) LLM incorporates this new "learning".

And that strikes me as a pretty reasonable long-term approach, if not one that fits within the constraints of the current gold rush.