New AI Research Improves Formal Theorem Proving Through Compiler Output Compression
A new research paper introduces a method to enhance the scalability of large language models in formal theorem proving. The approach addresses the high computational costs typically required for state-of-the-art performance by leveraging compiler outputs. Researchers observed that compilers transform numerous diverse proof attempts into a smaller collection of structured failure patterns. This compression enables a learning-to-refine framework that performs efficient learning and proof exploration. The method employs tree search to correct errors locally based on explicit verifier feedback, avoiding the expense of accumulating long histories of proof attempts. Extensive evaluations demonstrate that this technique consistently boosts the reasoning capabilities of base provers across different scales. The research was published on arXiv with the identifier 2604.18587v1. The announcement type is cross, indicating it spans multiple disciplines.
Key facts
- Large language models show significant potential in formal theorem proving
- State-of-the-art performance often requires prohibitive test-time compute
- Compilers map diverse proof attempts to compact structured failure modes
- A learning-to-refine framework leverages this compression for efficient learning
- Tree search corrects errors locally conditioned on explicit verifier feedback
- The method circumvents costs of accumulating long proof attempt histories
- Extensive evaluations show consistent amplification of base prover reasoning capabilities
- The approach achieves notable results across varying scales
Entities
Institutions
- arXiv