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Robust U1 RL

Robust-U1 is a unified Multimodal Large Language Model (MLLM) designed to self-recover corrupted visual content and perform reasoning over it. This framework addresses the performance degradation of MLLMs under...

TipoText modelVersiónBcalidad

Fuente del modelo

Tipo
Text model
Tamaño del archivo
320 MB–27,2 GB
Versión
vRevision 921e52e
Actualizado
1 nov 2026
Descargas
19
Fuente
Hugging Face

Descripción de la fuente

Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?

Robust-U1 is a unified Multimodal Large Language Model (MLLM) designed to self-recover corrupted visual content and perform reasoning over it. This framework addresses the performance degradation of MLLMs under real-world visual corruptions by equipping them with explicit visual self-recovery capabilities.

🏰 Pretrained checkpoints (reference)

CheckpointLinkNote
BAGEL-7B-MoTByteDance-Seed/BAGEL-7B-MoTUsed as initial weights for training.
Robust-U1Jiaqi-hkust/Robust-U1Final model for visual self-recovery and multimodal reasoning.
Robust-U1-RLJiaqi-hkust/Robust-U1-RLFine-tuned with reinforcement learning.
Robust-U1-SFTJiaqi-hkust/Robust-U1-SFTFine-tuned with supervised learning.

⭐️ Citation

If you find this repository useful, please cite our paper:

@inproceedings{
2026robustu,
title={Robust-U1: Can {MLLM}s Self-Recover Corrupted Visual Content for Robust Understanding?},
author={Jiaqi Tang, Jianmin Chen, Youyang Zhai, Wei Wei, Runtao Liu, Mengjie Zhao, Xiangyu Wu, Qingfa Xiao, Qifeng Chen},
booktitle={Forty-third International Conference on Machine Learning},
year={2026},
url={https://openreview.net/forum?id=I6W6cxVVts}
}

Requisitos

Modelos

1
  • ae.safetensors

    Encontrado

    VAE · 320 MB · SAFETENSORS · FP32 (tensor header) · Hugging Face · Jiaqi-hkust/Robust-U1-RL

Fuentes

1 fuente
Hugging FacePrincipalVersión 921e52e335552a6a5da3608fa902f7fca346cd24
huggingface.co/Jiaqi-hkust/Robust-U1-RL/tree/921e52e335552a6a5da3608fa902f7fca346cd24

Verificado 1 nov

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Artefactos del modelo

2 artefactos

ae.safetensors

safetensors · FP32 (tensor header) · 320 MB · SHA-256 afc8e28272cd…9e38 · Hugging Face

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model.safetensors

safetensors · BF16 (unverified) · 27,2 GB · SHA-256 92b569c135a3…2dd9 · Hugging Face

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Extractos de fuentes

3 extractos

--- license: mit pipeline_tag: any-to-any library_name: transformers --- # Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding? [Robust-U1](https://arxiv.org/abs/2606.08063) is a unified Multimodal Large Language Model (MLLM) designed to **self-recover corrupted visual content** and perform reasoning over it. This framework addresses the performance degradation of MLLMs under real-world visual corruptions by equipping them with explicit visual self-recovery capabilities. - **Paper:** [Robust-U1: Can MLLMs Self-Recover Corrupted Visual Content for Robust Understanding?](https://arxiv.org/abs/2606.08063) - **Repository:** [GitHub - jqtangust/Robust-U1](https://github.com/jqtangust/Robust-U1) - **Demo:** [Hugging Face Space](https://huggingface.co/spaces/Jiaqi-hkust/Robust-U1) ### 🏰 **Pretrained checkpoints (reference)** | Checkpoint | Link | Note | |:----------:|:----:|:----:| | BAGEL-7B-MoT | [ByteDance-Seed/BAGEL-7B-MoT](https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT) | Used as initial weights for training. | | **Robust-U1** | [Jiaqi-hkust/Robust-U1](https://huggingface.co/Jiaqi-hkust/Robust-U1) | Final model for visual self-recovery and multimodal reasoning. | | **Robust-U1-RL** | [Jiaqi-hkust/Robust-U1-RL](https://huggingface.co/Jiaqi-hkust/Robust-U1-RL) | Fine-tuned with reinforcement learning. | | **Robust-U1-SFT** | [Jiaqi-hkust/Robust-U1-SFT](https://huggingface.co/Jiaqi-hkust/Robust-U1-SFT) | Fine-tuned with supervised learning. | ## ⭐️ **Citation** If you find this repository useful, please cite our paper: ```bibtex @inproceedings{ 2026robustu, title={Robust-U1: Can {MLLM}s Self-Recover Corrupted Visual Content for Robust Understanding?}, author={Jiaqi Tang, Jianmin Chen, Youyang Zhai, Wei Wei, Runtao Liu, Mengjie Zhao, Xiangyu Wu, Qingfa Xiao, Qifeng Chen}, booktitle={Forty-third International Conference on Machine Learning}, year={2026}, url={https://openreview.net/forum?id=I6W6cxVVts} } ```

Source context: 19 downloads · 1 likes · Pipeline any-to-any · Library transformers · Repo Jiaqi-hkust/Robust-U1-RL

Source context: 7 downloads · 1 likes · Pipeline any-to-any · Library transformers · Repo Jiaqi-hkust/Robust-U1-RL