During model training, I imagine the model would benefit from some form of "self-reflection" at recurrent intervals, similar to human sleep. For a crude workflow, one could design the model to recall through auto-prompting onto a context window everything its learned that is relevant to the newly exposed training data, and then the model makes a rationale decision (following a constant pre-encoded prompt) to restate the information and classify it as factual or non-factual, and then this self-generated text is backpropagated to the model.

(Disclaimer: I follow ML research as a layman)