MMaDA: Multimodal Large Diffusion Language Models
Authors: name: Ling Yang, name: Ye Tian, name: Bowen Li, name: Xinchen Zhang, name: Ke Shen, name: Yunhai Tong, name: Mengdi Wang

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