World-Gymnast: Training Robots with Reinforcement Learning in a World Model

In this episode, we discuss World-Gymnast: Training Robots with Reinforcement Learning in a World Model by Ansh Kumar Sharma, Yixiang Sun, Ninghao Lu, Yunzhe Zhang, Jiarao Liu, Sherry Yang. The paper introduces World-Gymnast, a method that fine-tunes robot policies using reinforcement learning within a video-based world model conditioned on vision and language. This approach significantly outperforms traditional supervised finetuning and simulator-based RL in real-robot tasks, achieving up to 18x and 2x improvements, respectively. World-Gymnast also enables training on diverse instructions and novel scenes, offering a promising path for scalable robot learning outside controlled environments.