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arXiv · 2507.20199

StepFun-Prover Preview: Let's Think and Verify Step by Step

Shijie Shang·Ruosi Wan·Yue Peng·Yutong Wu·Xiong-hui Chen·Jie Yan·Xiangyu Zhang
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Abstract

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.

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Shijie Shang, Ruosi Wan, Yue Peng, Yutong Wu, Xiong-hui Chen, Jie Yan, Xiangyu Zhang. 2025-07-27. StepFun-Prover Preview: Let's Think and Verify Step by Step. https://arxiv.org/abs/2507.20199

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