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.

翻译：我们介绍混元证明器，这是一个基于混元7B模型微调的语言模型，用于在LEAN4环境下进行交互式自动定理证明。为缓解数据稀疏性问题，我们设计了一个可扩展框架，以低成本迭代合成数据。此外，我们设计了引导树搜索算法，以实现证明器高效的"系统2思维"。混元证明器在主要基准测试中取得了最先进的性能。具体而言，在miniF2F-test上达到了68.4%的通过率，而当前最优结果为65.9%。它在miniF2F-test中证明了4个国际数学奥林匹克命题（imo_1960_p2、imo_1962_p2、imo_1964_p2和imo_1983_p6）。为促进社区发展，我们将开源包含3万个合成实例的数据集，每个实例均包含自然语言原始问题、经自动形式化转换的命题陈述以及混元证明器生成的证明。