


ICLR 2024 Spotlight | No need to worry about intermediate steps, MUSTARD can generate high-quality mathematical inference data

The AIxiv column is a column where this site publishes academic and technical content. In the past few years, the AIxiv column of this site has received more than 2,000 reports, covering top laboratories from major universities and companies around the world, effectively promoting academic exchanges and dissemination. If you have excellent work that you want to share, please feel free to contribute or contact us for reporting. Submission email: liyazhou@jiqizhixin.com; zhaoyunfeng@jiqizhixin.com.

Paper title: MUSTARD: Mastering Uniform Synthesis of Theorem and Proof Data Paper link: https://openreview.net/forum?id=8xliOUg9EW Code link: https:/// /github.com/Eleanor-H/MUSTARD Dataset link: https://drive.google.com/file/d/1yIVAVqpkC2Op7LhisG6BJJ_-MavAMr1B/view Author homepage: https://eleanor- h.github.io/

MUSTARDSAUCE-valid: 5866 pieces of data verified by the Lean formal prover; MUSTARDSAUCE-invalid: failed to pass Lean 5866 pieces of data verified by the formal prover; MUSTARDSAUCE-random: 5866 pieces of random data; MUSTARDSAUCE-tt: all 28316 pieces of data generated by MUSTARD.




트랙 1-1(자동 비공식화): https://www.codabench.org/competitions/2436/ 트랙 1-2(자동 비공식화): https: //www.codabench .org/competitions/2484/ 트랙 2(자동 정리 생성 및 증명): https://www.codabench.org/competitions/2437/ 트랙 3(운영 연구 최적화의 코드 지원 자동 솔루션 문제): https://www.codabench.org/competitions/2438/
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