Wouldn't that just accelerate collapse? How much do you trust the outputs of the llm to provide trustworthy and valuable new information? I mean I understand distillation works. But that's much more structured and thoughtful than my sessions at least.
I was thinking of curated replay buffers, which would act like "dreams". To prevent collapse, the offline dataset would mix the new mid-term data with a baseline of anchor data (the original training distribution) so the model doesn't drift.
Also, we wouldn't train on the whole session. A separate critic module, like a reward model, would filter the KV cache to extract the high-value information, like a garbage collector before the LoRA.
That's just an idea though. Right now most research focuses on changing the architecture itself (TITAN, HOPE...) instead.
