Towards Robust Mathematical Reasoning
Thang Luong, Dawsen Hwang, Hoang H. Nguyen, Golnaz Ghiasi, Yuri Chervonyi, Insuk Seo, Junsu Kim, Garrett Bingham, Jonathan Lee, Swaroop Mishra, Alex Zhai, Clara Huiyi Hu, Henryk Michalewski, Jimin Kim, Jeonghyun Ahn, Junhwi Bae, Xingyou Song, Trieu H. Trinh, Quoc V. Le

TL;DR
This paper introduces IMO-Bench, a comprehensive set of challenging mathematical reasoning benchmarks modeled after the IMO, to evaluate and improve foundation models' proof-writing and problem-solving skills, achieving state-of-the-art results.
Contribution
The paper presents IMO-Bench, a new suite of advanced mathematical reasoning benchmarks for foundation models, including both problem-solving and proof-writing evaluations, with automatic grading tools.
Findings
Achieved 80.0% on IMO-AnswerBench
Achieved 65.7% on IMO-Proof Bench
Surpassed previous models by large margins
Abstract
Finding the right north-star metrics is highly critical for advancing the mathematical reasoning capabilities of foundation models, especially given that existing evaluations are either too easy or only focus on getting correct short answers. To address these issues, we present IMO-Bench, a suite of advanced reasoning benchmarks, vetted by a panel of top specialists and that specifically targets the level of the International Mathematical Olympiad (IMO), the most prestigious venue for young mathematicians. IMO-AnswerBench first tests models on 400 diverse Olympiad problems with verifiable short answers. IMO-Proof Bench is the next-level evaluation for proof-writing capabilities, which includes both basic and advanced IMO level problems as well as detailed grading guidelines to facilitate automatic grading. These benchmarks played a crucial role in our historic achievement of the…
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Taxonomy
TopicsMathematics, Computing, and Information Processing · Mathematics Education and Teaching Techniques · Machine Learning in Materials Science
