ReVisual-R1: An Open-Source 7B Multimodal Large Language Model (MLLMs) that Achieves Long, Accurate and Thoughtful Reasoning

ReVisual-R1 introduces a 7-billion-parameter open-source multimodal large language model (MLLM) designed to achieve long, accurate, and thoughtful reasoning across visual and textual inputs. Despite previous efforts to enhance multimodal reasoning using reinforcement learning (RL) techniques successful in text-only models, such as DeepSeek-R1, these approaches have faced challenges in effectively addressing the complex interactions between different data modalities, indicating the need for more specialized strategies. This development marks a significant step toward more sophisticated MLLMs capable of nuanced reasoning, leveraging tailored RL methods to improve the depth and length of generated outputs