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RL-FAT: Reinforcement Learning for Fair Adversarial Training

arXiv · AI, language, vision and robotics · article · Aug 29, 2026 · UTC

Deep neural networks remain highly vulnerable to adversarial perturbations, and adversarial training (AT) has become a widely used approach for improving robustness. However, improvements in average robust accuracy often mask substantial class-wise disparities: while some classes become more robust, others may remain disproportionately vulnerable under attack. This imbalance raises an important adversarial fairness concern, particularly in vision tasks where reliable robustness is expected across all categories. To address this challenge, we propose \textbf{RL-FAT}, a reinforcement-learning-in

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