Vision-R1 applies reinforcement learning techniques inspired by DeepSeek-R1 to multimodal large language models, introducing a cold-start initialization strategy to improve reasoning capabilities. The project releases model weights, training datasets (Vision-R1-cold and Vision-R1-rl), and demonstrates improved performance on math reasoning benchmarks including MathVista, MathVerse, and DynaMath across multiple model sizes.