CuDIP: Enhancing Theorem Proving in LLMs via Curriculum Learning-based Direct Preference Optimization

Shuming Shi, Ruobing Zuo, Gaolei He +3

Automated theorem proving (ATP) is one of the most challenging mathematical reasoning tasks for Large Language Models (LLMs). Most existing LLM-based ATP methods rely on supervised…