MMaDA: Multimodal Large Diffusion Language Models
Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, Mengdi Wang
2025-05-22
Summary
This paper talks about MMaDA, a new kind of AI model that can handle different types of information, like text and images, all at once and does better than older models by using a special training method.
What's the problem?
Most AI models struggle to work well with more than one type of data at the same time, and they often can't think through long, complicated problems in a way that makes sense.
What's the solution?
The researchers built MMaDA using a unified architecture that brings together different data types, trained it with a mix of long chain-of-thought examples, and used a special reinforcement learning technique to make it smarter and more flexible.
Why it matters?
This matters because it helps create AI that can understand and solve real-world problems that involve both words and pictures, making it more useful for things like education, research, and creative projects.
Abstract
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.