Comment by pastage
6 months ago
I know nothing about LLM training, but do you mean there is a solution to the issue of LLMs gaslighting each other? Sure this is a proven way of getting training data, but you can not get theorems and axioms right by generating different versions of them.
This is the paper: https://arxiv.org/abs/2411.02272
They won the 1st paper award: https://arcprize.org/2024-results
In their approach, the LLM generates inputs (images to be transformed) and solutions (Python programs that do the image transformations). The output images are created by applying the programs to the inputs.
So there's a constraint on the synthetic data here that keeps it honest -- the Python interpreter.
I believe the paper being referenced is “Scaling Data-Constrained Language Models” (https://arxiv.org/abs/2305.16264).
For correctness, you can use a solver to verify generated data.