R1-V is a project focused on enhancing the generalization capabilities of visual language models (VLMs). Using verified reward reinforcement learning (RLVR) technology, it significantly improves the generalization abilities of VLMs in visual counting tasks, particularly excelling in out-of-distribution (OOD) tests. The significance of this technology lies in its ability to efficiently optimize large-scale models at an extremely low cost (training costs as low as $2.62), offering new insights into the practical applications of visual language models. The project is based on improvements to existing VLM training methods, aiming to enhance model performance in complex visual tasks through innovative training strategies. Its open-source nature also makes it a vital resource for researchers and developers exploring and applying advanced VLM technologies.