[](https://arxiv.org/pdf/2601.02456)
[](https://github.com/InternRobotics/InternVLA-A1)
[](https://huggingface.co/datasets/InternRobotics/InternData-A1)
[](https://internrobotics.github.io/internvla-a1.github.io/)
InternVLA-A1 integrates understanding, generation, and action experts into a unified
model, which synergizes MLLMs' semantic reasoning with world-model-style dynamics prediction to guide action execution.
Building upon InternVL3 and Qwen3-VL, we instantiate InternVLA-A1 at 2B and 3B parameter scales. Covering different model scales and pre-training data configurations, we release the InternVLA-A1 series:
- [x] [InternVLA-A1-3B](https://huggingface.co/InternRobotics/InternVLA-A1-3B): pretrained on the large-scale, high-fidelity simulation data [InternData-A1](https://huggingface.co/datasets/InternRobotics/InternData-A1), together with open-source robot data (e.g. Agibot-World)
- [ ] [InternVLA-A1-3B-Pretrain-InternData-A1](https://huggingface.co/InternRobotics/InternVLA-A1-3B-Pretrain-InternData-A1): pretrained on InternData-A1 only
- [ ] [InternVLA-A1-2B-Pretrain-InternData-A1](https://huggingface.co/InternRobotics/InternVLA-A1-2B-Pretrain-InternData-A1): pretrained on InternData-A1 only
## 🔑 Key Features
Regarding model architecture, InternVLA-A1 employs a Mixture-of-Transformers (MoT) design to unifies scene understanding, visual foresight, and action execution into a single framework.
It synergizes MLLM's semantic understanding with world-model-style dynamic prediction, to "imagine" the future and guide adaptive actions.
Regarding training data, We pre-train InternVLA-A1 on hybrid synthetic-real datasets spanning InternData-A1 and open-source real-world data (e.g. Agibot-World). Our hybrid synthetic-real pre-training strategy combines
the scene diversity of simulation with the physical fidelity of real-world data.
## Usage
Please refer to our official repo [InternVLA-A1](https://github.com/InternRobotics/InternVLA-A1).
## Demonstrations
### âš¡ Dynamic Manipulation
InternVLA-A1 exhibits exceptional robustness in highly dynamic scenarios.
### 🤖 Daily tasks
InternVLA-A1 also demonstrates superior proficiency in dexterous and fine-grained manipulation.
## License and Citation
All the code within this repo are under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). Please consider citing our project if it helps your research.
```BibTeX
@article{contributors2026internvla_a1,
title={InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation},
author={InternVLA-A1 contributors},
journal={arXiv preprint arXiv:2601.02456},
year={2026}
}
```
## Acknowledgments
- [Lerobot](https://github.com/huggingface/lerobot)
- [openpi](https://github.com/Physical-Intelligence/openpi)
- [InternVL](https://github.com/OpenGVLab/InternVL)
- [Qwen3-VL](https://github.com/QwenLM/Qwen3-VL)
- [COSMOS](https://github.com/nvidia-cosmos)
