Detecting RLVR Training Data via Structural Convergence of Reasoning
This addresses benchmark contamination concerns for reasoning models, providing a tool to verify training data integrity, though it is incremental as it builds on existing detection methods.
The paper tackles the problem of detecting whether prompts were used in RLVR training data, which is challenging due to reward-based fine-tuning, by showing that RLVR induces rigid and similar generations for seen prompts. They introduce Min-kNN Distance, a black-box detector that quantifies this collapse and reliably distinguishes seen from unseen examples, outperforming existing baselines.
Reinforcement learning with verifiable rewards (RLVR) is central to training modern reasoning models, but the undisclosed training data raises concerns about benchmark contamination. Unlike pretraining methods, which optimize models using token-level probabilities, RLVR fine-tunes models based on reward feedback from self-generated reasoning trajectories, making conventional likelihood-based detection methods less effective. We show that RLVR induces a distinctive behavioral signature: prompts encountered during RLVR training result in more rigid and similar generations, while unseen prompts retain greater diversity. We introduce Min-$k$NN Distance, a simple black-box detector that quantifies this collapse by sampling multiple completions for a given prompt and computing the average of the $k$ smallest nearest-neighbor edit distances. Min-$k$NN Distance requires no access to the reference model or token probabilities. Experiments across multiple RLVR-trained reasoning models show that Min-$k$NN Distance reliably distinguishes RL-seen examples from unseen ones and outperforms existing membership inference and RL contamination detection baselines.