SimpleVLA-RL is a reinforcement learning framework designed to scale Vision-Language-Action model training, particularly under data scarcity conditions. It applies RL-based fine-tuning to VLAs that can perceive visual inputs, process language instructions, and generate actions for robotic or embodied AI tasks. The framework reportedly substantially outperforms supervised fine-tuning on both simulation and real-world tasks.