StepFun-Prover Preview: Let's Think and Verify Step by Step
This advances automated theorem proving and Math AI assistants, though it appears incremental as it builds on existing tool-integrated reasoning approaches.
The paper tackles automated theorem proving by developing StepFun-Prover, a large language model that uses reinforcement learning with tool-integrated reasoning to generate Lean 4 proofs, achieving a 70.0% pass@1 success rate on the miniF2F-test benchmark.
We present StepFun-Prover Preview, a large language model designed for formal theorem proving through tool-integrated reasoning. Using a reinforcement learning pipeline that incorporates tool-based interactions, StepFun-Prover can achieve strong performance in generating Lean 4 proofs with minimal sampling. Our approach enables the model to emulate human-like problem-solving strategies by iteratively refining proofs based on real-time environment feedback. On the miniF2F-test benchmark, StepFun-Prover achieves a pass@1 success rate of $70.0\%$. Beyond advancing benchmark performance, we introduce an end-to-end training framework for developing tool-integrated reasoning models, offering a promising direction for automated theorem proving and Math AI assistant.