Hallucinations in multimodal models occur when responses contradict image content. Apple Machine Learning Research analyzed how preference alignment reduces these errors in image understanding tasks. The study focuses on forcing models to prioritize visual evidence over internal linguistic biases. This provides a technical framework for reducing factual drift in vision-language systems.