Check out the (early) project and source code on GitHub.
Abstract: This paper introduces a methodology for generating high-quality, diverse training data for Language Models (LMs) in complex problem-solving domains. Our approach, termed “Reverse-Process Synthetic Data Generation” (RPSDG), inverts traditionally difficult problems to create an abundance of training examples with known solutions, e.