What is possible is to use a larger learning rate but this will be a hard trade-off with conversational capacities. Fine tuning is currently based on original texts with a synthetic prompt. The issues that people have noticed (repetitions, not remembering what was in the prompt) will be more significant if the learning rate is higher.
Maybe a solution will be to provide two different variant of the same model, one less immersive and more workable, and the other more immersive and buggy.
Yes it requires an extremely large diverse training set for the first unsupervised stage (pre-training). Then fine tune it on the smaller data set. But we may need to wait for the next generation of LLMs that incorporate planning algorithms so that it can better stay focused on its goal for whatever tasks we are asking it to do for research purposes. Otherwise we end up with this https://news.ycombinator.com/item?id=38418974