---
license: apache-2.0
language:
  - en
library_name: transformers
base_model:
  - Qwen/Qwen3-VL-4B-Thinking
pipeline_tag: image-text-to-text
tags:
  - visual-grounding
  - multimodal
  - qwen3-vl
  - reinforcement-learning
  - grpo
---

# EGM-Qwen3-VL-4B

<p align="center">
  <a href="https://nvlabs.github.io/EGM">[Project Page]</a> &nbsp;
  <a href="https://github.com/NVlabs/EGM">[Code]</a> &nbsp;
</p>

<div align="center">
  <img src="https://nvlabs.github.io/EGM/figure4.jpeg" width="90%"/>
</div>

## Model Summary

**EGM-Qwen3-VL-4B** is an efficient visual grounding model from the [EGM (Efficient Visual Grounding Language Models)](https://nvlabs.github.io/EGM) family. It is built on top of [Qwen3-VL-4B-Thinking](https://huggingface.co/Qwen/Qwen3-VL-4B-Thinking) and trained with a two-stage pipeline: supervised fine-tuning (SFT) followed by reinforcement learning (RL) using GRPO (Group Relative Policy Optimization).

EGM demonstrates that by increasing test-time computation, small vision-language models can **outperform much larger models** in visual grounding tasks while being significantly faster at inference.

## Key Results

- **90.9 average IoU** on the RefCOCO benchmark (vs. 87.2 for the base Qwen3-VL-4B-Thinking)
- **+3.7 IoU improvement** over the base model
- Outperforms Qwen3-VL-235B-A22B-Instruct (88.2 avg IoU) while being dramatically faster

### RefCOCO Benchmark Results

| Model | RefCOCO val | RefCOCO test-A | RefCOCO test-B | RefCOCO+ val | RefCOCO+ test-A | RefCOCO+ test-B | RefCOCOg val | RefCOCOg test | Avg |
|---|---|---|---|---|---|---|---|---|---|
| Qwen3-VL-4B-Thinking | 90.0 | 92.7 | 85.6 | 85.2 | 89.5 | 79.3 | 87.0 | 87.7 | 87.2 |
| **EGM-Qwen3-VL-4B** | **93.5** | **95.1** | **90.0** | **89.7** | **93.1** | **84.9** | **90.4** | **90.8** | **90.9** |

## How It Works

VLMs of different sizes often share the same visual encoder. Small models fall behind large models primarily due to a gap in **text understanding** capabilities — 62.8% of small model errors stem from complex prompts with multiple relational descriptions. EGM mitigates this gap by generating many mid-quality tokens (from small models) to match the performance of large VLMs that produce fewer but more expensive tokens.

### Training Pipeline

1. **SFT Stage**: A proprietary VLM generates detailed chain-of-thought reasoning steps for visual grounding training data. The base model is fine-tuned on this data. The SFT checkpoint is available as [nvidia/EGM-4B-SFT](https://huggingface.co/nvidia/EGM-4B-SFT).
2. **RL Stage**: GRPO is applied with a reward function combining IoU and task success metrics, further improving grounding accuracy.

## Quickstart

### Download

```bash
pip install -U huggingface_hub
huggingface-cli download nvidia/EGM-4B --local-dir ./models/EGM-4B
```

### Inference with SGLang

Launch the server:

```bash
pip install "sglang[all]>=0.5.5"

python -m sglang.launch_server \
    --model-path nvidia/EGM-4B \
    --chat-template=qwen3-vl \
    --port 30000
```

Send a visual grounding request:

```python
import openai
import base64

client = openai.Client(base_url="http://127.0.0.1:30000/v1", api_key="EMPTY")

# Load a local image as base64
with open("example.jpg", "rb") as f:
    image_base64 = base64.b64encode(f.read()).decode("utf-8")

response = client.chat.completions.create(
    model="nvidia/EGM-4B",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}},
                {"type": "text", "text": "Please provide the bounding box coordinate of the region this sentence describes: the person on the left."},
            ],
        }
    ],
    temperature=0.6,
    top_p=0.95,
    max_tokens=8192,
)
print(response.choices[0].message.content)
```

## Model Architecture

| Component | Details |
|---|---|
| Architecture | Qwen3VLForConditionalGeneration |
| Text Hidden Size | 2560 |
| Text Layers | 36 |
| Attention Heads | 32 (8 KV heads) |
| Text Intermediate Size | 9728 |
| Vision Hidden Size | 1024 |
| Vision Layers | 24 |
| Patch Size | 16 x 16 |
| Max Position Embeddings | 262,144 |
| Vocabulary Size | 151,936 |

## Citation

```bibtex
@article{zhan2026EGM,
    author = {Zhan, Guanqi and Li, Changye and Liu, Zhijian and Lu, Yao and Wu, Yi and Han, Song and Zhu, Ligeng},
    title = {EGM: Efficient Visual Grounding Language Models},
    booktitle = {arXiv},
    year = {2026}
}
```

## Acknowledgment

This repository benefits from [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL), [InternVL](https://github.com/OpenGVLab/InternVL), [verl](https://github.com/volcengine/verl) and [verl-internvl](https://github.com/Weiyun1025/verl-internvl).