# RL-FAT: Reinforcement Learning for Fair Adversarial Training

arXiv: https://arxiv.org/abs/2608.29247 · HTML: https://mlsanity.com/paper/2608.29247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 29 Aug 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 29 of 29)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks](https://mlsanity.com/paper/1706.02690/) (2017)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021)
- [OpenOOD: Benchmarking Generalized Out-of-Distribution Detection](https://mlsanity.com/paper/2210.07242/) (2022)
- [A Light Recipe to Train Robust Vision Transformers](https://mlsanity.com/paper/2209.07399/) (2022)
- [CFA: Class-wise Calibrated Fair Adversarial Training](https://mlsanity.com/paper/2303.14460/) (2023)
- [Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy](https://mlsanity.com/paper/2010.13365/) (2020)
- [Efficient and Effective Augmentation Strategy for Adversarial Training](https://mlsanity.com/paper/2210.15318/) (2022)
- [Prior-Guided Adversarial Initialization for Fast Adversarial Training](https://mlsanity.com/paper/2207.08859/) (2022)
- [Analysis and Applications of Class-wise Robustness in Adversarial Training](https://mlsanity.com/paper/2105.14240/) (2021)
- [FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting](https://mlsanity.com/paper/2204.00491/) (2022)
- [CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks](https://mlsanity.com/paper/2302.02213/) (2023)
- [WAT: Improve the Worst-class Robustness in Adversarial Training](https://mlsanity.com/paper/2302.04025/) (2023)
- [Robust Models are less Over-Confident](https://mlsanity.com/paper/2210.05938/) (2022)
- [Neural Architecture Design and Robustness: A Dataset](https://mlsanity.com/paper/2306.06712/) (2023)
- [Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?](https://mlsanity.com/paper/2112.01601/) (2021)
- [Towards Fairness-Aware Adversarial Learning](https://mlsanity.com/paper/2402.17729/) (2024)
- [As large as it gets: Learning infinitely large Filters via Neural Implicit Functions in the Fourier Domain](https://mlsanity.com/paper/2307.10001/) (2023)
- [Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation](https://mlsanity.com/paper/2312.05508/) (2023)
- [FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training](https://mlsanity.com/paper/2410.23142/) (2024)
- [Towards Class-wise Robustness Analysis](https://mlsanity.com/paper/2411.19853/) (2024)
- [Reinforcement Based Learning on Classification Task Could Yield Better Generalization and Adversarial Accuracy](https://mlsanity.com/paper/2012.04353/) (2020)

## Abstract (arXiv)

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 RL-FAT, a reinforcement-learning-inspired fair adversarial training framework that uses policy-gradient based feedback from adversarial predictions. RL-FAT interprets the prediction distribution as a policy and combines correctness-based prediction rewards with class-wise value estimates to compute class-specific advantages for policy-gradient optimization. This enables the model to adaptively focus on class-wise misclassification. Furthermore, we introduce a fairness-emphasis adversarial loss that assigns stronger training pressure to classes with high adversarial loss, thereby mitigating class-wise robustness disparity. By combining reinforcement-driven adaptation with fairness-emphasis regularization, RL-FAT improves adversarial robustness while promoting a more balanced robustness distribution across classes. Extensive experiments demonstrate that our method achieves competitive robust accuracy and substantially reduces class-wise robustness imbalance compared with standard adversarial training baselines.