MMaDA: Multimodal Large Diffusion Language Models
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. The approach
is distinguished by three key innovations: (i) MMaDA adopts a unified diffusion
architecture with a shared probabilistic formulation and a modality-agnostic
design, eliminating the need for modality-specific components. This
architecture ensures seamless integration and processing across different data
types. (ii) We implement a mixed long chain-of-thought (CoT) fine-tuning
strategy that curates a unified CoT format across modalities. By aligning
reasoning processes between textual and visual domains, this strategy
facilitates cold-start training for the final reinforcement learning (RL)
stage, thereby enhancing the model's ability to handle complex tasks from the
outset. (iii) We propose UniGRPO, a unified policy-gradient-based RL algorithm
specifically tailored for diffusion foundation models. Utilizing diversified
reward modeling, UniGRPO unifies post-training across both reasoning and
generation tasks, ensuring consistent performance improvements. Experimental
results demonstrate that MMaDA-8B exhibits strong generalization capabilities
as a unified multimodal foundation model. It surpasses powerful models like
LLaMA-3-7B and Qwen2-7B in textual reasoning, outperforms Show-o and SEED-X in
multimodal understanding, and excels over SDXL and Janus in text-to-image
generation. These achievements highlight MMaDA's effectiveness in bridging the
gap between pretraining and post-training within unified diffusion
architectures, providing a comprehensive framework for future research and
development. We open-source our code and trained models at:
https://github.com/Gen-Verse/MMaDA
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Ling Yang
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2025-05-26T08:22:31.113Z
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Ling Yang
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Ye Tian
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tyfeld
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Bowen Li
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LBW
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YucYux
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Xinchen Zhang
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Xinchen Zhang
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Ke Shen
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2025-05-26T08:22:28.227Z
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shenke
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Yunhai Tong
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Mengdi Wang
MMaDA: Multimodal Large Diffusion Language Models
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. The approach
is distinguished by three key innovations: (i) MMaDA adopts a unified diffusion
architecture with a shared probabilistic formulation and a modality-agnostic
design, eliminating the need for modality-specific components. This
architecture ensures seamless integration and processing across different data
types. (ii) We implement a mixed long chain-of-thought (CoT) fine-tuning
strategy that curates a unified CoT format across modalities. By aligning
reasoning processes between textual and visual domains, this strategy
facilitates cold-start training for the final reinforcement learning (RL)
stage, thereby enhancing the model's ability to handle complex tasks from the
outset. (iii) We propose UniGRPO, a unified policy-gradient-based RL algorithm
specifically tailored for diffusion foundation models. Utilizing diversified
reward modeling, UniGRPO unifies post-training across both reasoning and
generation tasks, ensuring consistent performance improvements. Experimental
results demonstrate that MMaDA-8B exhibits strong generalization capabilities
as a unified multimodal foundation model. It surpasses powerful models like
LLaMA-3-7B and Qwen2-7B in textual reasoning, outperforms Show-o and SEED-X in
multimodal understanding, and excels over SDXL and Janus in text-to-image
generation. These achievements highlight MMaDA's effectiveness in bridging the
gap between pretraining and post-training within unified diffusion
architectures, providing a comprehensive framework for future research and
development. We open-source our code and trained models at:
https://github.com/Gen-Verse/MMaDA
MMaDA, a multimodal diffusion foundation model, achieves superior performance through a unified architecture, mixed long chain-of-thought fine-tuning, and a unified policy-gradient-based RL algorithm.
multimodal diffusion foundation models unified diffusion architecture modality-agnostic design mixed long chain-of-thought fine-tuning cold-start training reinforcement learning UniGRPO policy-gradient-based RL algorithm diversified reward modeling generalization capabilities textual reasoning multimodal understanding text-to-image generation