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--- |
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license: mit |
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library_name: transformers |
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pipeline_tag: any-to-any |
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--- |
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# MMaDA-8B-Base |
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We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal understanding, and text-to-image generation. MMaDA is distinguished by three key innovations: |
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1. MMaDA adopts a unified diffusion architecture with a shared probabilistic formulation and a modality-agnostic design, eliminating the need for modality-specific components. |
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2. MMaDA introduces a mixed long chain-of-thought (CoT) fine-tuning strategy that curates a unified CoT format across modalities. |
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3. MMaDA adopts a unified policy-gradient-based RL algorithm, which we call UniGRPO, tailored for diffusion foundation models. Utilizing diversified reward modeling, UniGRPO unifies post-training across both reasoning and generation tasks, ensuring consistent performance improvements. |
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[Paper](https://arxiv.org/abs/2505.15809) | [Code](https://github.com/Gen-Verse/MMaDA) | [Demo](https://huggingface.co/spaces/Gen-Verse/MMaDA) |
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# Citation |
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``` |
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@article{yang2025mmada, |
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title={MMaDA: Multimodal Large Diffusion Language Models}, |
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author={Yang, Ling and Tian, Ye and Li, Bowen and Zhang, Xinchen and Shen, Ke and Tong, Yunhai and Wang, Mengdi}, |
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journal={arXiv preprint arXiv:2505.15809}, |
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year={2025} |
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} |
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``` |
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