EGM-Qwen3-VL-8B is the flagship model of the EGM (Efficient Visual Grounding Language Models) family. It is built on top of Qwen3-VL-8B-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

91.4 average IoU on the RefCOCO benchmark (vs. 87.8 for the base Qwen3-VL-8B-Thinking)
+3.6 IoU improvement over the base model
Outperforms Qwen3-VL-235B-A22B-Instruct (88.2 avg IoU) and Qwen3-VL-235B-A22B-Thinking (90.7 avg IoU)
5.9x faster inference than Qwen3-VL-235B (737ms vs 4,320ms average latency)
18.9x faster than Qwen3-VL-235B-Thinking

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-8B-Thinking	91.0	92.5	86.6	86.2	91.2	80.5	87.8	88.6	87.8
EGM-Qwen3-VL-8B	93.9	95.6	91.2	90.5	93.5	86.3	90.8	91.4	91.4
Qwen3-VL-235B-A22B-Instruct	90.4	94.6	82.2	86.4	92.1	78.5	90.5	90.5	88.2
Qwen3-VL-235B-A22B-Thinking	93.4	94.1	90.6	89.5	91.4	85.2	90.4	90.5	90.7

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

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-8B-SFT.
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-8B --local-dir ./models/EGM-8B

Inference with SGLang

Launch the server:

bash

pip install "sglang[all]>=0.5.5"

python -m sglang.launch_server \
    --model-path nvidia/EGM-8B \
    --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-8B",
    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	4096
Text Layers	36
Attention Heads	32 (8 KV heads)
Text Intermediate Size	12,288
Vision Hidden Size	1152
Vision Layers	27
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, InternVL, verl and verl-internvl.