Spatial-SSRL-7B
📖Paper| 🏠Github |🤗Spatial-SSRL-7B Model | 🤗Spatial-SSRL-3B Model | 🤗Spatial-SSRL-Qwen3VL-4B Model | 🤗Spatial-SSRL-81k Dataset | 📰Daily Paper
Spatial-SSRL-7B is a large vision-language model targeting spatial understanding, built on the base of Qwen2.5-VL-7B. It's optimized by applying Spatial-SSRL, a lightweight self-supervised reinforcement learning paradigm which can scale RLVR efficiently. The model demonstrates strong spatial intelligence while preserving the original general visual capabilities of the base model.
📢 News
- 🚀 [2026/04/05] We have released the training code of Spatial-SSRL.
- 🚀 [2026/02/25] We have released the 🤗Spatial-SSRL-3B Model, initialized from Qwen2.5-VL-3B-Instruct.
- 🚀 [2026/02/21] Our work has been accepted by CVPR 2026.
- 🚀 [2025/11/24] We have released the 🤗Spatial-SSRL-Qwen3VL-4B Model, initialized from Qwen3-VL-4B-Instruct.
- 🚀 [2025/11/03] Now you can try out Spatial-SSRL-7B on 🤗Spatial-SSRL Space.
- 🚀 [2025/11/03] We have released the 🤗Spatial-SSRL-7B Model, and 🤗Spatial-SSRL-81k Dataset.
- 🚀 [2025/11/02] We have released the 🏠Spatial-SSRL Repository.
🌈 Overview
We are thrilled to introduce Spatial-SSRL, a novel self-supervised RL paradigm aimed at enhancing LVLM spatial understanding. By optimizing Qwen2.5-VL-7B with Spatial-SSRL, the model exhibits stronger spatial intelligence across seven spatial understanding benchmarks in both image and video settings.
💡 Highlights
- 🔥 Highly Scalable: Spatial-SSRL uses ordinary raw RGB and RGB-D images instead of richly-annotated public datasets or manual labels for data curation, making it highly scalable.
- 🔥 Cost-effective: Avoiding the need for human labels or API calls for general LVLMs throughout the entire pipeline endows Spatial-SSRL with cost-effectiveness.
- 🔥 Lightweight: Prior approaches for spatial understanding heavily rely on annotation of external tools, incurring inherent errors in training data and additional cost. In constrast, Spatial-SSRL is completely tool-free and can easily be extended to more self-supervised tasks.
- 🔥 Naturally Verifiable: Intrinsic supervisory signals determined by pretext objectives are naturally verifiable, aligning Spatial-SSRL well with the RLVR paradigm.
📊 Results
We train Qwen2.5-VL-3B and Qwen2.5-VL-7B with our Spatial-SSRL paradigm and the experimental results across seven spatial understanding benchmarks are shown below.
🛠️ Usage
To directly experience Spatial-SSRL-7B, you can try it out on 🤗Spatial-SSRL Space!
Here we provide a code snippet for you to start a simple trial of Spatial-SSRL-7B on your own device. You can download the model from 🤗Spatial-SSRL-7B Model before your trial!
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model_path = "internlm/Spatial-SSRL-7B" #You can change it to your own local path if deployed already
img_path = "examples/eg1.jpg"
question = "Consider the real-world 3D locations of the objects. Which object has a higher location? A. yellow bear kite B. building"
#We recommend using the format prompt to make the inference consistent with training
format_prompt = "\n You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \\boxed{}."
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_path, torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_path)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": img_path,
},
{"type": "text", "text": question + format_prompt},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
generated_ids = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Model Response:", output_text)
Cases
✒️Citation
If you find our model useful, please kindly cite:
@article{liu2025spatial,
title={Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning},
author={Liu, Yuhong and Zhang, Beichen and Zang, Yuhang and Cao, Yuhang and Xing, Long and Dong, Xiaoyi and Duan, Haodong and Lin, Dahua and Wang, Jiaqi},
journal={arXiv preprint arXiv:2510.27606},
year={2025}
}
📄 License
Usage and License Notices: The data and code are intended and licensed for research use only.