nvidia/EGM-4B-SFT

EGM-Qwen3-VL-4B-SFT

<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-SFT is the supervised fine-tuning (SFT) checkpoint from the first stage of the EGM (Efficient Visual Grounding Language Models) training pipeline. It is built on top of Qwen3-VL-4B-Thinking.

This is an intermediate checkpoint intended for further reinforcement learning training. For the final model with best performance, see nvidia/EGM-4B.

Training Details

SFT Stage

In the SFT stage, a proprietary VLM generates detailed chain-of-thought reasoning steps for visual grounding training data. The base Qwen3-VL-4B-Thinking model is then fine-tuned on this reasoning-augmented data to learn structured visual grounding with explicit reasoning.

This SFT checkpoint serves as the initialization for the subsequent RL stage (GRPO), which yields the final EGM-4B model.

How to Use for RL Training

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

Then follow the installation and RL training instructions in the EGM repository.

Model Architecture

Related Models

Citation

@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.