Summary of Lean-star: Learning to Interleave Thinking and Proving, by Haohan Lin et al.
Lean-STaR: Learning to Interleave Thinking and Proving
by Haohan Lin, Zhiqing Sun, Sean Welleck, Yiming Yang
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel framework for training language models to produce informal thoughts prior to each step of a proof is presented in this paper. The approach, called Lean-STaR, uses retrospective ground-truth tactics to generate synthetic thoughts for training and then applies expert iteration to fine-tune the model on correct proofs sampled and verified using the Lean solver. The framework boosts the model’s theorem-proving capabilities by incorporating human-like thought processes that are not present in formal proofs. This is achieved by having the model directly generate thoughts prior to predicting tactics in each proof step. Experiments show that Lean-STaR achieves state-of-the-art results on the miniF2F-test benchmark within the Lean theorem proving environment, outperforming base models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way for computers to learn and prove mathematical theorems. It’s like teaching a computer to think through problems step by step, just like humans do. The researchers developed a framework called Lean-STaR that helps computers generate thoughts before making predictions about math proofs. This improves their ability to actually solve the proofs correctly. The paper shows that this approach works well and can even outperform other methods. |
