Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning

The introduction of Spatial-SSRL marks a significant advancement in the field of spatial understanding for Large Vision-Language Models (LVLMs). Unlike traditional methods that rely on expensive supervision and specialized tools, this self-supervised reinforcement learning approach utilizes ordinary RGB and RGB-D images to derive verifiable signals. This innovation not only enhances the capabilities of LVLMs but also opens up new possibilities for scaling and improving spatial understanding in various applications, making it a noteworthy development in AI research.