Summary of Hunyuanprover: a Scalable Data Synthesis Framework and Guided Tree Search For Automated Theorem Proving, by Yang Li et al.
HUNYUANPROVER: A Scalable Data Synthesis Framework and Guided Tree Search for Automated Theorem Proving
by Yang Li, Dong Du, Linfeng Song, Chen Li, Weikang Wang, Tao Yang, Haitao Mi
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces HunyuanProver, an interactive automatic theorem proving (ATP) model fine-tuned from the Hunyuan 7B language model for LEAN4. To address data sparsity issues, a scalable framework iteratively synthesizes low-cost data. Guided tree search algorithms enable effective “system 2 thinking” in the prover. HunyuanProver achieves state-of-the-art (SOTA) performance on major benchmarks, including a pass rate of 68.4% on the miniF2F-test and proof of four IMO statements. The authors will release a dataset of 30k synthesized instances to benefit the community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new tool called HunyuanProver that helps computers prove mathematical theorems. It uses an existing language model to improve its abilities. To make it work better, the team created a way to generate lots of practice problems and developed special search techniques. HunyuanProver is very good at proving math theorems, beating the current best results. The researchers will share their dataset of 30,000 practice problems with the community. |
Keywords
» Artificial intelligence » Language model
