A Theoretical Framework for Self-Play Theorem Proving Algorithms
Researchers have developed a theoretical framework to understand how self-play algorithms can improve theorem-proving capabilities in large language models. The framework formalizes theorems as a graph and demonstrates that a prover-conjecturer system can exponentially grow the set of proved theorems under certain conditions. To address issues with artificially complex theorems, the paper proposes a diversity measure and an improved conjecturing algorithm that maximizes this diversity by analyzing theorem similarity. AI
IMPACT Provides a theoretical foundation for improving AI's logical reasoning and formal verification capabilities.