Check out the (early) project and source code on GitHub.

The idea

Some problems are easy in one direction and hard in the other. Taking a derivative is mechanical. Finding an antiderivative can require genuine creativity. Generating a random expression and verifying a proof is easy. Discovering the proof is hard.

RPSDG (Reverse-Process Synthetic Data Generation) exploits this asymmetry. Run the easy direction with full step-by-step work, then reverse the result to get a hard problem with a known solution. You end up with process-supervised training data: not just the answer, but the entire derivation.

Richard Sutton’s “The Bitter Lesson” argues that methods scaling with compute and data will eventually win. The bottleneck is high-quality data. A lot of the world’s data is latent, the processes that generated it are not written down. In math, the way a proof was discovered is usually hidden behind a polished presentation. RPSDG is one way to manufacture that hidden process data.