Poster
in
Workshop: Workshop on Reasoning and Planning for Large Language Models

MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard Perturbations

Kaixuan Huang ⋅ Jiacheng Guo ⋅ Zihao Li ⋅ Xiang Ji ⋅ Jiawei Ge ⋅ Wenzhe Li ⋅ Yingqing Guo ⋅ Tianle Cai ⋅ Hui Yuan ⋅ Runzhe Wang ⋅ Yue Wu ⋅ Ming Yin ⋅ Shange Tang ⋅ Yangsibo Huang ⋅ Chi Jin ⋅ Xinyun Chen ⋅ Chiyuan Zhang ⋅ Mengdi Wang

Project Page [ OpenReview]

Abstract

A genuinely robust reasoning model should be able to function correctly when the problem statement is modified out-of-training-distribution. Prior work has shown that language models struggle on mathematical benchmarks when questions undergo simple perturbations -- modifications that still preserve the underlying reasoning patterns of the solutions. However, no work has explored hard perturbations, which fundamentally change the nature of the problem so that the original solution steps do not apply. To bridge the gap, we construct MATH-P-Simple and MATH-P-Hard via simple perturbation and hard perturbation, respectively. Each consists of 279 perturbed math problems derived from level-5 (hardest) problems in the MATH dataset (Hendrycks et. al., 2021). We observe significant performance drops on MATH-P-Hard across various models, including o1-mini (-16.49%) and gemini-2.0-flash-thinking (-12.9%). We also raise concerns about a novel form of memorization where models blindly apply learned problem-solving skills without assessing their applicability to modified contexts. This issue is amplified when using original problems for in-context learning. We call for research efforts to address this challenge, which is critical for developing more robust and reliable reasoning models.

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