Teaching LLMs to explicitly identify and apply core proof techniques—rather than jumping straight to full proofs—significantly improves their ability to solve complex mathematical problems.
This paper tackles informal theorem proving by teaching language models to recognize core proof techniques. The authors create a structured dataset that breaks down proofs into key insights and sketches, then train models using a multi-stage approach that mirrors how humans learn math—starting with basic proofs and progressing to deeper reasoning.