Model Misalignment

Model misalignment refers to discrepancies between a model's intended behavior and its actual performance, arising from various sources including incomplete or inaccurate training data, flawed reward functions, and limitations in model architecture. Current research focuses on identifying and mitigating these misalignments across diverse applications, examining their impact on areas such as vision-language models, reinforcement learning agents, and privacy-preserving machine learning. Understanding and addressing model misalignment is crucial for ensuring the reliability, safety, and ethical deployment of increasingly sophisticated AI systems, impacting fields ranging from robotics to data security.