MMaDA 8B MixCoT 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: 1. MMaDA adopts a unified diffusion architecture with a shared probabilistic formulation and a modality agnostic design, eliminating the need for modality specific components. 2. MMaDA introduces a mixed long chain of thought (CoT) fine tuning strategy that curates a unified CoT format across modalities. 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. Compared to MMaDA 8B Base, MMaDA 8B MixCoT exhibits better instruction following capabilities and more stable CoT generation performance. Paper Code Demo Citation
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