AI for Math Workshop @ ICML 2024

Accepted papers (21)

Fetched from OpenReview (v2) on 2026-06-10.

1. ☆Advancing LLM Reasoning Generalists with Preference Trees

   Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan, Huimin Chen, Ruobing Xie, Yankai Lin, Zhenghao Liu, Bowen Zhou, Hao Peng, Zhiyuan Liu, Maosong Sun · PDF

2. ☆AI for an inverse problem: Physical model solving quantum gravity

   Koji Hashimoto, Koshiro Matsuo, Masaki Murata, Gakuto Ogiwara, Daichi Takeda · PDF

3. ☆DACO: Towards Application-Driven and Comprehensive Data Analysis via Code Generation

   Xueqing Wu, Rui Zheng, Jingzhen Sha, Te-Lin Wu, Hanyu Zhou, Tang Mohan, Kai-Wei Chang, Nanyun Peng, Haoran Huang · PDF

4. ☆Distilling LLMs’ Decomposition Abilities into Compact Language Models

   Denis Tarasov, Kumar Shridhar · PDF

5. ☆Efficient Linear System Solver with Transformers

   Max Vladymyrov, Johannes von Oswald, Nolan Andrew Miller, Mark Sandler · PDF

6. ☆GeomVerse: A Systematic Evaluation of Large Models for Geometric Reasoning

   Mehran Kazemi, Hamidreza Alvari, Ankit Anand, Jialin Wu, Xi Chen, Radu Soricut · PDF

7. ☆Large Language Models Can Self-Correct with Minimal Effort

   Zhenyu Wu, Qingkai Zeng, Zhihan Zhang, Zhaoxuan Tan, Chao Shen, Meng Jiang · PDF

8. ☆Lean4trace: Data augmentation for neural theorem proving in Lean

   Vasilii Nesterov, Yermek Kapushev, Mikhail Burtsev · PDF

9. ☆Learning Efficient Recursive Numeral Systems via Reinforcement Learning

   Jonathan David Thomas, Andrea Silvi, Devdatt Dubhashi, Emil Carlsson, Moa Johansson · PDF

10. ☆Learning to Reason by Failing: Offline RL on Sub-optimal Rollouts Scales Synthetic Data by 8x

    Amrith Setlur, Saurabh Garg, Xinyang Geng, Naman Garg, Virginia Smith, Aviral Kumar · PDF

11. ☆Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving

    Aniket Rajiv Didolkar, Anirudh Goyal, Nan Rosemary Ke, Siyuan Guo, Michal Valko, Timothy P Lillicrap, Danilo Jimenez Rezende, Yoshua Bengio, Michael Curtis Mozer, Sanjeev Arora · PDF

12. ☆More Details, Please: Improving Autoformalization with More Detailed Proofs

    Guillem Tarrach, Albert Q. Jiang, Daniel Raggi, Wenda Li, Mateja Jamnik · PDF

13. ☆Pre-Calc: Learning to Use the Calculator Improves Numeracy in Language Models

    Vishruth Veerendranath, Vishwa Shah, Kshitish Ghate · PDF

14. ☆Progress or Regress? Self-Improvement Reversal in Post-training

    Ting Wu, Xuefeng Li, Pengfei Liu · PDF

15. ☆Progressive-Hint Prompting Improves Reasoning in Large Language Models

    Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, Yu Li · PDF

16. ☆PutnamBench: A Multilingual Competition-Mathematics Benchmark for Formal Theorem-Proving

    George Tsoukalas, Jasper Lee, John Jennings, Jimmy Xin, Michelle Ding, Michael Jennings, Amitayush Thakur, Swarat Chaudhuri · PDF

17. ☆Smart Vision-Language Reasoners

    Denisa Roberts, Lucas Roberts · PDF

18. ☆Specify What? A Case-Study using GPT-4 and Formal Methods For Specification Synthesis

    George Granberry, Wolfgang Ahrendt, Moa Johansson · PDF

19. ☆Teaching Large Language Models to Reason with Reinforcement Learning

    Alexander Havrilla, Yuqing Du, Sharath Chandra Raparthy, Christoforos Nalmpantis, Jane Dwivedi-Yu, Eric Hambro, Sainbayar Sukhbaatar, Roberta Raileanu · PDF

20. ☆Technical Report for ICML 2024 Automated Math Reasoning Challenge: Solving Optimization Problems with Open Source Large Language Model

    Duc M. Nguyen, Sungahn Ko · PDF

21. ☆VerityMath: Advancing Mathematical Reasoning by Self-Verification Through Unit Consistency

    Vernon Toh Yan Han, Ratish Puduppully, Nancy F. Chen · PDF