RL for VLMs

This field studies how reinforcement learning can improve models that interpret images or video alongside language, and sometimes take actions in visual environments. Common approaches include group-relative policy optimization, PPO, and reinforcement learning from verifiable rewards, with feedback based on answer correctness, visual evidence, intermediate reasoning, preferences, or tool use. Applications range from image and video question answering, spatial reasoning, and medical analysis to navigation and robotic manipulation. A central challenge is ensuring that better scores reflect genuine use of visual evidence rather than language shortcuts, while designing reliable rewards and efficient training that generalize beyond the training setting.