Detecting Data Contamination from Reinforcement Learning Post-training
  for Large Language Models
Authors: name: Yongding Tao, name: Tian Wang, name: Yihong Dong, name: Huanyu Liu, name: Kechi Zhang, name: Xiaolong Hu, name: Ge Li
Organization: Peking University

Data contamination poses a significant threat to the reliable evaluation of
Large Language Models (LLMs). This issue arises when benchmark samples may
inadvertently appear in training sets, compromising the validity of reported
performance. While detection methods have been developed for the pre-training
and Supervised Fine-Tuning stages, a critical research gap exists for the
increasingly significant phase of Reinforcement Learning (RL) post-training. As
RL post-training becomes pivotal for advancing LLM reasoning, the absence of
specialized contamination detection methods in this paradigm presents a
critical vulnerability. To address this, we conduct the first systematic study
of data detection within RL post-training scenario and propose Self-Critique.
Our method is motivated by a key observation: after RL phase, the output
entropy distribution of LLMs tends to collapse into highly specific and sparse
modes. Self-Critique probes for the underlying policy collapse, i.e., the
model's convergence to a narrow reasoning path, which causes this entropy
reduction. To facilitate this research, we also introduce RL-MIA, a benchmark
constructed to simulate this specific contamination scenario. Extensive
experiments show that Self-Critique significantly outperforms baseline methods
across multiple models and contamination tasks, achieving an AUC improvement of
up to 30%. Whereas existing methods are close to a random guess for RL-phase
contamination, our method makes detection possible.