Instructions to use MuMing0102/VGPO-RL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MuMing0102/VGPO-RL-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="MuMing0102/VGPO-RL-7B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("MuMing0102/VGPO-RL-7B")
model = AutoModelForImageTextToText.from_pretrained("MuMing0102/VGPO-RL-7B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MuMing0102/VGPO-RL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MuMing0102/VGPO-RL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MuMing0102/VGPO-RL-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/MuMing0102/VGPO-RL-7B

SGLang

How to use MuMing0102/VGPO-RL-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MuMing0102/VGPO-RL-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MuMing0102/VGPO-RL-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MuMing0102/VGPO-RL-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MuMing0102/VGPO-RL-7B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use MuMing0102/VGPO-RL-7B with Docker Model Runner:
```
docker model run hf.co/MuMing0102/VGPO-RL-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for VGPO-RL-7B

📖 Overview of VGPO

Standard RLVR methods treat every generated token equally, broadcasting a single reward signal indiscriminately. This leads to signal dilution — generic text tokens receive the same reinforcement as critical visually-grounded reasoning steps. Meanwhile, temporal visual forgetting causes attention to visual inputs to progressively decay as reasoning chains extend.

VGPO addresses these issues through three key mechanisms:

Visual Attention Compensation (VAC): Uses the inherent hidden-state similarity between generated tokens and image tokens as a Visual Focus Score to localize visual activations without external supervision. A progressive incentive schedule counteracts temporal visual forgetting in later reasoning steps.
Intra-Trajectory Re-weighting: At the token level, dynamically re-weights advantages by visual focus scores to amplify learning from visually-grounded tokens.
Inter-Trajectory Re-weighting: At the trajectory level, prioritizes rollouts with superior visual accumulation, favoring trajectories that sustain consistent visual grounding.

🔗 Model Sources

Github Repository: VGPO
ArXiv Paper: 2604.09349

📕 Training Datasets

Split	Dataset	Link
Train	ViRL39K	PAPOGalaxy/PAPO_ViRL39K_train
Val	MMK12	PAPOGalaxy/PAPO_MMK12_test

📊 Evaluation

We follow the evaluation script of Look-Back. All results are reported as average accuracy with inference temperature 0.0.

Supported Evaluation Benchmarks

Benchmark	Focus Domain
MathVista	General Mathematical & Geometric Reasoning
MathVerse	General Mathematical & Geometric Reasoning
WeMath	General Mathematical & Geometric Reasoning
MMK12	General Mathematical & Geometric Reasoning
GeoMath	General Mathematical & Geometric Reasoning
Geometry3K	General Mathematical & Geometric Reasoning
LogicVista	Vision-dependent Multimodal Reasoning
SuperClevr Counting	Vision-dependent Multimodal Reasoning
MMMU-Pro	Vision-dependent Multimodal Reasoning
MathVerse-V	Vision-dependent Multimodal Reasoning

✍️ Citation

If you find this codebase useful in your research, please consider giving us a star ⭐ and citing our work 📝:

@article{wang2026vgpo,
  title={Visually-Guided Policy Optimization for Multimodal Reasoning}, 
  author={Zengbin Wang and Feng Xiong and Liang Lin and Xuecai Hu and Yong Wang and Yanlin Wang and Man Zhang and Xiangxiang Chu},
  journal={arXiv preprint arXiv:2604.09349},
  year={2026}
}