We introduce HunyuanProver, an language model finetuned from the Hunyuan 7B for interactive automatic theorem proving with LEAN4. To alleviate the data sparsity issue, we design a scalable framework to iterative synthesize data with low cost. Besides, guided tree search algorithms are designed to enable effective ``system 2 thinking`` of the prover. HunyuanProver achieves state-of-the-art (SOTA) performances on major benchmarks. Specifically, it achieves a pass of 68.4% on the miniF2F-test compared to 65.9%, the current SOTA results. It proves 4 IMO statements (imo_1960_p2, imo_1962_p2}, imo_1964_p2 and imo_1983_p6) in miniF2F-test. To benefit the community, we will open-source a dataset of 30k synthesized instances, where each instance contains the original question in natural language, the converted statement by autoformalization, and the proof by HunyuanProver.
@article{arxiv.2412.20735,
title = {HunyuanProver: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving},
author = {Yang Li and Dong Du and Linfeng Song and Chen Li and Weikang Wang and Tao Yang and Haitao Mi},
journal= {arXiv preprint arXiv:2412.20735},
year = {2025}
}