Summary of Internlm2.5-stepprover: Advancing Automated Theorem Proving Via Expert Iteration on Large-scale Lean Problems, by Zijian Wu et al.
InternLM2.5-StepProver: Advancing Automated Theorem Proving via Expert Iteration on Large-Scale LEAN Problems
by Zijian Wu, Suozhi Huang, Zhejian Zhou, Huaiyuan Ying, Jiayu Wang, Dahua Lin, Kai Chen
First submitted to arxiv on: 21 Oct 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 research paper explores the application of Large Language Models (LLMs) in mathematical theorem proving, particularly when utilizing formal languages like LEAN. The expert iteration paradigm involves a pre-defined dataset with numerous mathematical problems, which LLMs attempt to prove and refine their capabilities through self-training on discovered proofs. This study proposes using large-scale LEAN problem datasets for expert iteration, achieving log-linear trends between solved problem amounts, proof lengths, and CPU usage. A critic model is trained to select relatively easy problems for policy models, guiding the search for deeper proofs. The InternLM2.5-StepProver achieves state-of-the-art results on various benchmarks, including MiniF2F, Lean-Workbook-Plus, ProofNet, and Putnam. Specifically, it proves 65.9% of problems on the MiniF2F-test and 17.0% of problems in Lean-Workbook-Plus, demonstrating a significant improvement compared to previous releases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special computers called Large Language Models (LLMs) to help solve math problems. The LLMs are trained on huge datasets of math problems and then try to prove the answers. This helps them get better at solving math problems over time. The researchers used a big dataset with over 20,000 math problems and found that the LLMs got faster and more accurate as they practiced. They also developed a special model that can help guide the LLMs in finding deeper proofs of math problems. The results are impressive, with the LLMs solving over 65% of problems on one test and over 17% of problems on another. This research could help make it easier for computers to solve complex math problems. |
Keywords
» Artificial intelligence » Self training
