Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised
  Reinforcement Learning
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.
6908284d812eca10f9cc5e92
Yuhong Liu
6908284d812eca10f9cc5e93
Beichen Zhang
6908284d812eca10f9cc5e94
Yuhang Zang
claimed_verified
2025-11-03T20:52:07.168Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63859cf3b2906edaf83af9f0/kajwuVzd4pDucSPlwghxo.png
Yuhang Zang
yuhangzang
user
yuhangzang
6908284d812eca10f9cc5e95
Yuhang Cao
6908284d812eca10f9cc5e96
Long Xing
claimed_verified
2025-11-27T10:15:30.093Z
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/avatars/a5b30f1c14cb2b2c633076719f824ec0.svg
Long Xing
long-xing1
user
long-xing1
6908284d812eca10f9cc5e97
Xiaoyi Dong
6908284d812eca10f9cc5e98
Haodong Duan
6908284d812eca10f9cc5e99
Dahua Lin
6908284d812eca10f9cc5e9a
Jiaqi Wang
claimed_verified
2026-02-17T15:48:28.640Z
64b4eec4faa3181a5eab9c46
/avatars/bcc9bf5cbf67546ad2b4c9ec8b96ac96.svg
Jiaqi Wang
myownskyW7
user
myownskyW7
Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised
  Reinforcement Learning
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.
Spatial-SSRL, a self-supervised reinforcement learning paradigm, enhances spatial understanding in Large Vision-Language Models using verifiable signals from RGB or RGB-D images without human annotation.
Large Vision-Language Models self-supervised RL pretext tasks shuffled patch reordering flipped patch recognition cropped patch inpainting regional depth ordering relative 3D position prediction spatial reasoning spatial understanding benchmarks