When an LLM creates an output, it seemingly has no way to check if its output was valid. Therefore it wouldn't be able to back-propagate any changes to the weights is used to create that output.
Right now, I suspect that all weight modification is done by training on input data, as that can (generally) be assumed to be human and valid.
But perhaps it does have some way of checking if its output was good or not, and being modified based off of it. For instance, there is the thumb up and down button on ChatGPT which could be used for that.
Do LLM's modify their neural weights based off of their own answers? If so, how?