Reasoning or Memorization? Unreliable Results of Reinforcement Learning
  Due to Data Contamination
Authors: name: Mingqi Wu, name: Zhihao Zhang, name: Qiaole Dong, name: Zhiheng Xi, name: Jun Zhao, name: Senjie Jin, name: Xiaoran Fan, name: Yuhao Zhou, name: Yanwei Fu, name: Qin Liu, name: Songyang Zhang, name: Qi Zhang

The reasoning capabilities of large language models (LLMs) have been a
longstanding focus of research. Recent works have further enhanced these
capabilities using reinforcement learning (RL), with many new methods claiming
significant improvements with minimal or no external supervision. Surprisingly,
some studies even suggest that random or incorrect reward signals can enhance
reasoning performance. However, these breakthroughs are mostly reported on the
Qwen2.5 model family and evaluated on well-known benchmarks such as MATH-500,
AMC, and AIME, while failing to achieve similar gains on other models like
Llama, which warrants further investigation. Our analysis shows that although
Qwen2.5 achieves strong mathematical reasoning performance, its pretraining on
large-scale web corpora makes it vulnerable to data contamination in popular
benchmarks. As a result, results derived from these benchmarks may be
unreliable. To address this, we introduce a generator that produces fully
synthetic arithmetic problems of arbitrary length and difficulty, yielding a
clean dataset we call RandomCalculation. Using these leakage-free datasets, we
show that only accurate reward signals consistently improve performance, while
noisy or incorrect signals do not. We advocate for evaluating RL methods on
uncontaminated benchmarks and across diverse model families to ensure
trustworthy conclusions.