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MMaDA - A multimodal diffusion model developed by ByteDance in collaboration with Princeton University and others.

MMaDA (Multimodal Large Diffusion Language Models) is a multimodal diffusion model developed by Princeton University, Tsinghua University, Peking University, and ByteDance. It supports cross-text reasoning, multimodal understanding, and text...

What is MMaDA?

MMaDA (Multimodal Large Diffusion Language Models) is a multimodal diffusion model developed by Princeton University, Tsinghua University, Peking University, and ByteDance. It supports superior performance across multiple domains, including cross-text reasoning, multimodal understanding, and text-to-image generation. The model employs a unified diffusion architecture with modality-agnostic design, eliminating the need for specific modality components. It introduces a hybrid long-chain inference (CoT) fine-tuning strategy, unifies the cross-modal CoT format, and introduces UniGRPO, a unified policy gradient reinforcement learning algorithm for the diffusion base model. Based on diverse reward modeling, it unifies post-training for inference and generation tasks, ensuring consistent performance improvement. MMaDA demonstrates performance exceeding existing models on multiple tasks, providing a new direction for the development of multimodal AI.

Main functions of MMaDA

  • Text generationIt can generate high-quality text content, supporting everything from simple text descriptions to complex reasoning tasks.
  • Multimodal understandingIt understands and processes the combination of text and images, supports detailed descriptions of image content, and image-based question answering.
  • Text to Image GenerationIt generates corresponding images based on text descriptions, supporting the generation from abstract concepts to specific scenes.
  • Complex reasoning tasksIt supports handling complex tasks such as mathematical problems and logical reasoning, providing detailed reasoning processes and accurate answers.
  • Cross-modal collaborative learningBased on a unified architecture and training strategy, it enables collaborative learning and optimization between text and image modalities.

Technical Principles of MMaDA

  • Unified diffusion architectureUsing a unified diffusion architecture, based on shared probability formulas and modality-agnostic design, the need for modality-specific components is eliminated. The model can seamlessly process text and image data. During the pre-training phase, the model is jointly trained on text and image modalities based on a masked label prediction task. The model learns to recover original data from noisy data.
  • Mixed Long Chain-of-Thought (CoT) Fine-tuning StrategyThis approach aligns inference processes across different tasks using a unified CoT format. The CoT format includes step-by-step inference trajectories and final results, enabling the model to generate detailed inference processes. Fine-tuning is performed using diverse inference data, including mathematical problems, logical reasoning, and multimodal inference tasks. This allows the model to excel in handling complex tasks.
  • Unified Policy Gradient Reinforcement Learning Algorithm (UniGRPO)UniGRPO is based on diverse reward modeling, unifying inference and post-training for generation tasks. Reward functions include correctness, format, and CLIP scores, ensuring excellent model performance across various tasks. UniGRPO uses multi-step denoising learning, allowing the model to learn from partially noisy data and better utilize the multi-step generation capabilities of diffusion models.

MMaDA's project address

Application scenarios of MMaDA

  • Content creationGenerate text and images for writing, design, and artistic creation.
  • Educational SupportIt provides personalized learning materials and detailed problem-solving steps to support teaching.
  • Intelligent Customer ServiceBased on text and image interaction, it answers user questions and improves the service experience.
  • HealthcareIt assists in medical image analysis, provides health advice, and supports medical decision-making.
  • Entertainment GamesGenerate game content and augmented reality experiences to enrich entertainment and interaction.
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