We will use Grok 3.5 (maybe we should call it 4), which has advanced reasoning, to rewrite the entire corpus of human knowledge, adding missing information and deleting errors.
Then retrain on that.
Far too much garbage in any foundation model trained on uncorrected data.
More Context
There’s some nuance.
Using LLMs to augment data, especially for fine tuning (not training the base model), is a sound method. The Deepseek paper using, for instance, generated reasoning traces is famous for it.
Another is using LLMs to generate logprobs of text, and train not just on the text itself but on the *probability a frontier LLM sees in every ‘word.’ This is called distillation, though there’s some variation and complication. This is also great because it’s more power/time efficient. Look up Arcee models and their distillation training kit for more on this, and code to see how it works.
There are some papers on “self play” that can indeed help LLMs.
But yes, the “dumb” way, aka putting data into a text box and asking an LLM to correct it, is dumb and dumber, because:
You introduce some combination of sampling errors and repetition/overused word issues, depending on the sampling settings. There’s no way around this with old autoregressive LLMs.
You possibly pollute your dataset with “filler”
In Musk's specific proposition, it doesn’t even fill knowledge gaps the old Grok has.
In other words, Musk has no idea WTF he’s talking about. It’s the most boomer, AI Bro, not techy ChatGPT user thing he could propose.