A new self-supervised reinforcement learning framework aims to enhance spatial reasoning in Large Reasoning Models by focusing on internal consistency rather than relying on labeled data. This approach shows promise in achieving accuracy comparable to traditional supervised methods while maintaining generalization across various tasks.