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ScienceOne-AI/
S1-VL-32B-RL

Name: S1-VL-32B-RL
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S1-VL-32B: Scientific Multimodal Reasoning Model

中文版 | English

🔬 Introduction

S1-VL-32B is a multimodal large language model for scientific domains, developed by the ScienceOne AI team at the Chinese Academy of Sciences. It natively supports two reasoning paradigms — Scientific Reasoning and Thinking with Images — and achieves state-of-the-art performance across multiple mainstream scientific multimodal evaluation benchmarks.

Scientific Reasoning: Chain-of-thought-based multimodal scientific reasoning, designed for the analysis and solving of complex, multi-step problems.
Thinking with Images: Enables the model to actively invoke code tools during the reasoning process to perform image operations — including cropping, zooming, image enhancement, bounding box annotation, and keypoint marking — before generating responses.

We have established a cross-disciplinary data processing pipeline that conducts multi-dimensional utility evaluation and filtering of visual reasoning trajectories to ensure the quality of training reasoning trajectories.

We adopt a four-stage progressive post-training procedure to progressively unlock the scientific reasoning capabilities of S1-VL-32B:

Stage 1 - Scientific Reasoning SFT: Large-scale multimodal instruction data spanning multiple disciplines — including mathematics, physics, chemistry, astronomy, earth sciences, and biology — is used for mixed training to enhance the model's scientific visual understanding and logical reasoning abilities, laying a solid foundation for academic figure Q&A, medical image analysis, chemical structure recognition, and related tasks.
Stage 2 - Thinking-with-Images Cold-Start SFT: The Thinking with Images reasoning paradigm is introduced. Through joint training with high-quality scientific reasoning curriculum learning data and image-thinking data, the model acquires the ability to perform image operations via code during inference. This approach yields particularly outstanding performance in interpreting dense scientific charts, high-resolution remote sensing imagery, microscopic images, and astronomical observation data (S1-VL-32B-SFT).
Stage 3 - Scientific Reasoning RL: Based on the SAPO algorithm and a multi-task scientific reward function, reinforcement learning is applied to challenging scientific multimodal reasoning samples to push beyond the performance ceiling of the SFT stage.
Stage 4 - Thinking-with-Images RL: Based on the SAPO algorithm and a four-dimensional composite reward function, the model's image operation invocation timing and quality are further optimized, enabling stable and efficient multi-round visual reasoning (S1-VL-32B-RL).

🔥 [NEW] Technical report released: S1-VL: Scientific Multimodal Reasoning Model with Thinking-with-Images
🔥 [NEW] Stage 3 and Stage 4 reinforcement learning training added; S1-VL-32B-RL model weights updated.

📂 Model Weights

Model	Parameters	HuggingFace	ModelScope
S1-VL-32B-SFT	32B	🤗 Download	🤖 Download
S1-VL-32B-RL	32B	🤗 Download	🤖 Download

🏆 Evaluation Results

The evaluation covers 2 dimensions and 13 benchmarks. The Scientific Multimodal Reasoning dimension includes MMMU, SFE, MathVision, Physics, ScienceOlympiad, VRSBench-MINI, GMAI-MMBench, and Galaxy-10-DECaLS, spanning mathematics, physics, medicine, remote sensing, astronomy, and other professional fields. The Image Manipulation Reasoning dimension includes HRBench-4K, HRBench-8K, MME-RealWorld-CN, MME-RealWorld-Lite, and V*, focusing on high-resolution image understanding and real-world visual reasoning.

S1-VL-32B demonstrates outstanding overall competitiveness across the aforementioned evaluations. In scientific multimodal reasoning tasks, the model achieves significant advantages on multiple authoritative benchmarks — including MMMU, MathVision, and VRSBench-MINI — surpassing its base model Qwen3-VL-32B in overall performance, while remaining highly competitive against open-source models with substantially larger parameter scales (e.g., Qwen3-VL-235B, Intern-S1) as well as closed-source flagship models (e.g., Gemini 2.5 Pro, GPT-5). In image operation reasoning tasks, S1-VL-32B ranks first across all five benchmark evaluations, comprehensively outperforming models of comparable and larger scales, while also surpassing dedicated "Thinking with Images" models such as Thyme-VL and Skywork-R1V4. These results fully validate its ability to achieve efficient, high-quality multimodal reasoning at the 32B parameter scale.

🧠 Case Study

The following presents reasoning examples of S1-VL-32B operating in Thinking with Images mode. When processing a low-resolution cervical CT image, S1-VL-32B proactively invokes code tools during its reasoning process to perform cropping and magnification on the region of interest. By obtaining a clearer local image, the model then combines the enhanced visual information with its internal knowledge to complete the reasoning.

📁 More cases are available in CASES.md.

🚀 Quick Start

1. Install Dependencies

bash

# Requires vLLM >= 0.11.0
pip install -U vllm
pip install qwen-vl-utils==0.0.14

2. Start the vLLM Service

bash

vllm serve ScienceOne-AI/S1-VL-32B-RL \
    --tensor-parallel-size 4 \
    --max-model-len 32768 \
    --limit-mm-per-prompt image=15 \
    --reasoning-parser deepseek_r1 \
    --enable-prefix-caching \
    --gpu-memory-utilization 0.95 \
    --port 9200

3. Multimodal Reasoning Mode

python

from openai import OpenAI
import base64

client = OpenAI(api_key="EMPTY", base_url="http://localhost:9200/v1")

with open("path/to/your/image.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode("utf-8")

response = client.chat.completions.create(
    model="ScienceOne-AI/S1-VL-32B-RL",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_data}"}},
                {"type": "text", "text": "Please describe the physical phenomenon shown in the image and derive the relevant equations."},
            ],
        }
    ],
    temperature=0.2,
    max_tokens=16384,
)

# The reasoning process is in the reasoning_content field
print("Thinking process:\n", response.choices[0].message.reasoning_content)
print("\nFinal answer:\n", response.choices[0].message.content)

4. Thinking with Images Mode

Thinking with Images mode requires deploying a code sandbox to support the model invoking code tools during reasoning for image operations (cropping, zooming, enhancement, annotation, etc.).

Step 1: Deploy the Code Sandbox

We recommend deploying the AIO Sandbox with Docker:

bash

git clone https://github.com/agent-infra/sandbox
cd sandbox
# Mount the host image directory into the container
docker run -d \
    --name twi-sandbox \
    -p 18081:18081 \
    -v /data/images:/mnt/data/images \   # host path → sandbox path
    sandbox:latest

The mount path must match the path configuration in the FastAPI service.

Step 2: Start the Thinking with Images FastAPI Service

Download twi_server.py and update the path configuration at the top of the file:

python

CHAT_API        = "http://localhost:9200/v1/chat/completions"  # vLLM address
JUPYTER_API     = "http://localhost:18081/v1/jupyter"          # Sandbox address
HOST_IMG_DIR    = "/data/images"     # ← Host image directory (must match docker -v mount)

Start the service:

bash

pip install fastapi uvicorn httpx pillow
python twi_server.py   # Listens on port 10044

Step 3: Call the Thinking with Images Endpoint

python

import httpx
import base64

with open("path/to/your/image.png", "rb") as f:
    image_b64 = base64.b64encode(f.read()).decode("utf-8")

messages = [
    {"type": "text", "text": "Please carefully analyze this scientific image."},
    {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_b64}"}},
]

response = httpx.post(
    "http://localhost:10044/process",
    json={
        "messages": messages,
        "image_path_list": ["/data/images/your_image.png"],  # Absolute host path
    },
    timeout=300,
)

result = response.json()

# The final answer is the last message with role="assistant"
final = [m for m in result["messages"] if m["role"] == "assistant"][-1]
print(final["content"])

📄 Citation

If you use S1-VL-32B in your research, please cite:

latex

@article{li2026s1vl,
  title     = {S1-VL: Scientific Multimodal Reasoning Model with Thinking-with-Images},
  author    = {Li, Qingxiao and Xu, Lifeng and Wang, QingLi and Bai, Yudong and Ou, Mingwei and Hu, Shu and Xu, Nan},
  journal   = {arXiv preprint arXiv:2604.21409},
  year      = {2026},
}

📜 License

This project is released under the Apache 2.0 License.

🙏 Acknowledgements

We thank the open-source communities and pioneering works of Qwen3-VL and AIO Sandbox for laying the foundation for the scientific multimodal reasoning research behind S1-VL-32B.