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---
license: apache-2.0
language:
- en
tags:
- spatial-reasoning
- multimodal
- vision-language
- scene-graph
- reinforcement-learning
base_model: Qwen/Qwen2.5-VL-3B-Instruct
pipeline_tag: image-text-to-text
---
# SpatialThinker-3B
<p align="center">
<a href="https://arxiv.org/abs/2511.07403">
<img src="https://img.shields.io/badge/arXiv-2511.07403-b31b1b.svg" alt="arXiv">
</a>
<a href="https://hunarbatra.com/SpatialThinker">
<img src="https://img.shields.io/badge/🌐%20Project%20Page-blue.svg" alt="Project Page">
</a>
<a href="https://github.com/hunarbatra/SpatialThinker">
<img src="https://img.shields.io/badge/GitHub-Repository-black.svg" alt="GitHub">
</a>
</p>
**SpatialThinker-3B** is a 3D-aware multimodal large language model (MLLM) trained with reinforcement learning to integrate structured spatial grounding with multi-step reasoning. The model simulates human-like spatial perception by constructing a scene graph of task-relevant objects and spatial relations, and reasoning towards an answer via dense spatial rewards.
## Model Description
- **Base Model**: Qwen2.5-VL-3B-Instruct
- **Training**: GRPO (Group Relative Policy Optimization) with dense spatial rewards
- **Training Data**: STVQA-7K (7,587 spatial VQA samples)
- **Authors**: Hunar Batra, Haoqin Tu, Hardy Chen, Yuanze Lin, Cihang Xie, Ronald Clark
- **Institutions**: University of Oxford, UC Santa Cruz
## Key Features
- **Structured Spatial Reasoning**: Constructs question-focused scene subgraphs with objects, bounding boxes, and relations
- **Dense Spatial Rewards**: Multi-objective reward function enforcing format, count, accuracy, and spatial grounding
- **9 Spatial Reasoning Categories**: Relations, reach, size, orientation, instance location, depth, distance, count, and existence
- **Outperforms GPT-4o**: On spatial understanding benchmarks while using only 7K training samples
## Inference Template
Use the following template for inference:
```
You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.
Image size: {Width} x {Height}
```
## Usage
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from PIL import Image
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"OX-PIXL/SpatialThinker-3B",
torch_dtype="auto",
device_map="auto"
)
processor = AutoProcessor.from_pretrained("OX-PIXL/SpatialThinker-3B")
# Load image
image = Image.open("your_image.jpg")
width, height = image.size
# Prepare prompt with template
template = f"""You FIRST observe the image in <observe> </observe> tags, then visualise the relevant scene graph in <scene> </scene> tags, followed by thinking about the reasoning process as an internal monologue within <think> </think> tags and then provide the final answer. The final answer MUST BE put within <answer> </answer> tags, and only return the final choice including the correct option and answer within the answer tags, e.g., <answer> (A) cat </answer>.
Image size: {width} x {height}"""
question = "Where is the cat relative to the couch? (A) on top of (B) in front of (C) behind (D) beside"
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": template + "\n\n" + question},
],
}
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], return_tensors="pt").to(model.device)
generated_ids = model.generate(**inputs, max_new_tokens=1024)
output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(output)
```
## Citation
```bibtex
@misc{batra2025spatialthinkerreinforcing3dreasoning,
title={SpatialThinker: Reinforcing 3D Reasoning in Multimodal LLMs via Spatial Rewards},
author={Hunar Batra and Haoqin Tu and Hardy Chen and Yuanze Lin and Cihang Xie and Ronald Clark},
year={2025},
eprint={2511.07403},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2511.07403},
}
```
## Links
- 📄 **Paper**: [arXiv:2511.07403](https://arxiv.org/abs/2511.07403)
- 🌐 **Project Page**: [hunarbatra.com/SpatialThinker](https://hunarbatra.com/SpatialThinker)
- 💻 **GitHub**: [github.com/hunarbatra/SpatialThinker](https://github.com/hunarbatra/SpatialThinker)
- 🤗 **Dataset**: [OX-PIXL/STVQA-7K](https://huggingface.co/datasets/OX-PIXL/STVQA-7K)
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