> A big issue with constant retraining is going to be the self referential consumption of it's own generated material as training material That's just a hypoth

> A big issue with constant retraining is going to be the self referential consumption of it's own generated material as training material That's just a hypothetical scenario. Usually LLMs don't generate text used indiscriminately to train new models. In creative tasks, the generated texts are the result of multiple explorations and corrections, filtered by the user, then posted online and commented on by other people. So there is plenty of correction and feedback mixed into the process. A child model could learn something from this feedback. Then there is code and math, where it is possible to run tests or double check by a different method. These tasks could be iteratively improved by LLMs by incorporating the feedback. Another field ripe with feedback signals is using LLMs in games and simulations, where you can specify a goal easily but finding a solution requires search and learning. And a related application was RLHF, where a preference model was trained on human judgments and used to fine-tune a LLM. That preference model can be used to filter bad training data as well. To generalize, LLMs alone can't do it, but LLMs with external systems can get feedback and improve. The garbage-in-garbage-out scenario can be avoided with a bit of external feedback.