I am also worried about LLM "indbreeding." When I finetuned successive generations of ESRGAN on its own output (as I essentially wanted to use it for img2img),

I am also worried about LLM "indbreeding." When I finetuned successive generations of ESRGAN on its own output (as I essentially wanted to use it for img2img), it would amplify tiny oddities and artifacts that, I would later find out, were in the training data. Tiny noise splotches, "swirls" and distorted line edges blew up. And I was careful... I pixel peeped the dataset as best I could before starting training. Human language is obviously different, but I still fear oddities or biases will start popping up when the base models train on large fractions of their own data. And by the time we find out, it will be near impossible to filter out. But continuing the analogy, maybe a diverse base model population is a good way to avoid that issue?