DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural
  Language and Reinforcement Learning
Theorem proving serves as a major testbed for evaluating complex reasoning
abilities in large language models (LLMs). However, traditional automated
theorem proving (ATP) approaches rely heavily on formal proof systems that
poorly align with LLMs' strength derived from informal, natural language
knowledge acquired during pre-training. In this work, we propose DeepTheorem, a
comprehensive informal theorem-proving framework exploiting natural language to
enhance LLM mathematical reasoning. DeepTheorem includes a large-scale
benchmark dataset consisting of 121K high-quality IMO-level informal theorems
and proofs spanning diverse mathematical domains, rigorously annotated for
correctness, difficulty, and topic categories, accompanied by systematically
constructed verifiable theorem variants. We devise a novel reinforcement
learning strategy (RL-Zero) explicitly tailored to informal theorem proving,
leveraging the verified theorem variants to incentivize robust mathematical
inference. Additionally, we propose comprehensive outcome and process
evaluation metrics examining proof correctness and the quality of reasoning
steps. Extensive experimental analyses demonstrate DeepTheorem significantly
improves LLM theorem-proving performance compared to existing datasets and
supervised fine-tuning protocols, achieving state-of-the-art accuracy and
reasoning quality. Our findings highlight DeepTheorem's potential to
fundamentally advance automated informal theorem proving and mathematical
exploration.
68391c6dc4405ad056a9ff79
Ziyin Zhang
claimed_verified
2025-06-02T07:47:09.492Z
6430bdd8cd31d174a9f900fb
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/Y9SPnRfpKSbYc7MhNdP-H.jpeg
Ziyin Zhang
user
Geralt-Targaryen
68391c6dc4405ad056a9ff7a
Jiahao Xu
68391c6dc4405ad056a9ff7b
Zhiwei He
68391c6dc4405ad056a9ff7c
Tian Liang
68391c6dc4405ad056a9ff7d
Qiuzhi Liu
68391c6dc4405ad056a9ff7e
Yansi Li
68391c6dc4405ad056a9ff7f
Linfeng Song
claimed_verified
2025-07-11T08:09:08.774Z
64c94eddcb2f1bf0e7db5a4d
/avatars/f7e2532d3c85d5e5b5a02c579ea68c3a.svg
Linfeng Song
user
freesunshine0316
68391c6dc4405ad056a9ff80
Zhengwen Liang
claimed_verified
2025-06-02T07:47:06.128Z
62ffa3f8311cad266f9af236
/avatars/203dac40bc546ee25a01d8715a4b3049.svg
Zhenwen Liang
user
invokerliang
68391c6dc4405ad056a9ff81
Zhuosheng Zhang
68391c6dc4405ad056a9ff82
Rui Wang
68391c6dc4405ad056a9ff83
Zhaopeng Tu
68391c6dc4405ad056a9ff84
Haitao Mi
68391c6dc4405ad056a9ff85
Dong Yu
DeepTheorem: Advancing LLM Reasoning for Theorem Proving Through Natural
  Language and Reinforcement Learning
Theorem proving serves as a major testbed for evaluating complex reasoning
abilities in large language models (LLMs). However, traditional automated
theorem proving (ATP) approaches rely heavily on formal proof systems that
poorly align with LLMs' strength derived from informal, natural language
knowledge acquired during pre-training. In this work, we propose DeepTheorem, a
comprehensive informal theorem-proving framework exploiting natural language to
enhance LLM mathematical reasoning. DeepTheorem includes a large-scale
benchmark dataset consisting of 121K high-quality IMO-level informal theorems
and proofs spanning diverse mathematical domains, rigorously annotated for
correctness, difficulty, and topic categories, accompanied by systematically
constructed verifiable theorem variants. We devise a novel reinforcement
learning strategy (RL-Zero) explicitly tailored to informal theorem proving,
leveraging the verified theorem variants to incentivize robust mathematical
inference. Additionally, we propose comprehensive outcome and process
evaluation metrics examining proof correctness and the quality of reasoning
steps. Extensive experimental analyses demonstrate DeepTheorem significantly
improves LLM theorem-proving performance compared to existing datasets and
supervised fine-tuning protocols, achieving state-of-the-art accuracy and
reasoning quality. Our findings highlight DeepTheorem's potential to
fundamentally advance automated informal theorem proving and mathematical
exploration.
DeepTheorem enhances LLM theorem-proving through a large-scale natural language dataset and a tailored reinforcement learning strategy, achieving state-of-the-art results in informal theorem proving.
DeepTheorem automated theorem proving ATP natural language informal theorem-proving large-scale benchmark dataset IMO-level theorems reinforcement learning RL-Zero verified theorem variants proof correctness reasoning quality