Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised
  Reinforcement Learning
Authors: name: Yuhong Liu, name: Beichen Zhang, name: Yuhang Zang, name: Yuhang Cao, name: Long Xing, name: Xiaoyi Dong, name: Haodong Duan, name: Dahua Lin, name: Jiaqi Wang
Organization: Intern Large Models

Spatial understanding remains a weakness of Large Vision-Language Models
(LVLMs). Existing supervised fine-tuning (SFT) and recent reinforcement
learning with verifiable rewards (RLVR) pipelines depend on costly supervision,
specialized tools, or constrained environments that limit scale. We introduce
Spatial-SSRL, a self-supervised RL paradigm that derives verifiable signals
directly from ordinary RGB or RGB-D images. Spatial-SSRL automatically
formulates five pretext tasks that capture 2D and 3D spatial structure:
shuffled patch reordering, flipped patch recognition, cropped patch inpainting,
regional depth ordering, and relative 3D position prediction. These tasks
provide ground-truth answers that are easy to verify and require no human or
LVLM annotation. Training on our tasks substantially improves spatial reasoning
while preserving general visual capabilities. On seven spatial understanding
benchmarks in both image and video settings, Spatial-SSRL delivers average
accuracy gains of 4.63% (3B) and 3.89% (7B) over the Qwen2.5-VL baselines. Our
results show that simple, intrinsic supervision enables RLVR at scale and
provides a practical route to stronger spatial intelligence in LVLMs.