VLM-R1 is a reinforcement learning-based visual-language model focused on visual understanding tasks, such as Referring Expression Comprehension (REC). By combining Reinforcement Learning (R1) and Supervised Fine-Tuning (SFT) methods, this model demonstrates excellent performance on both in-domain and out-of-domain data. The main advantages of VLM-R1 include its stability and generalization ability, enabling it to excel in various visual-language tasks. Built upon Qwen2.5-VL, the model leverages advanced deep learning techniques like Flash Attention 2 to enhance computational efficiency. VLM-R1 aims to provide an efficient and reliable solution for visual-language tasks, suitable for applications requiring precise visual understanding.