this post was submitted on 01 Nov 2025
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That's certainly something I've observed myself training GANs on their own output. It's definitely a problem for the stupid (like Tech Bros).
But it doesn't happen like you think, as long as the augmentations are clever, and their scope is narrow. Hence the success of several recent distillations and 'augmented' LLMs, and the failure of huge dataset trains like Llama4.
...And synthetic data generation/augmentation is getting clever, and is already being used in newer trains. See this, or newer papers if your search for them on arixv: https://github.com/qychen2001/Awesome-Synthetic-Data
Or Nvidia's HUGE focus on this, combining it with their work in computer graphics: https://www.nvidia.com/en-us/use-cases/synthetic-data-physical-ai/