Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios

The article's title suggests a focus on evaluating the robustness and reliability of reward models, particularly in scenarios where the input data is altered or noisy. This is a crucial area of research for ensuring the safety and dependability of AI systems that rely on reward functions, such as reinforcement learning agents. The use of the term "perturbed scenarios" indicates an investigation into how well the reward model performs when faced with variations or imperfections in the data it receives. The source being ArXiv suggests this is a peer-reviewed research paper.