Recently, Princeton University, ByteDance, Tsinghua University, and Peking University have teamed up to create something big by launching a multi-modal large model named MMaDA! This is no ordinary AI; it claims to give AI the ability for "deep thinking" and can "transform" between text, images, and even complex reasoning tasks. Its performance surpasses familiar models like GPT-4, Gemini, and even SDXL!
You might think current multi-modal models are already impressive, able to describe images or generate pictures based on text. But MMaDA tells us this is far from enough! Traditional models often require independent components or complex hybrid mechanisms to handle different modalities, similar to a "multi-tool box." While it has everything, switching between tools can be somewhat awkward.
The MMaDA team aims to break down these barriers, allowing AI to truly integrate!
MMaDA's Three "Black Technologies": Making AI Not Just Understand, but Also "Think Through"!
MMaDA stands out thanks to its three core innovations:
Unified Diffusion Architecture: A Modality "Blind Box," Handling Everything Seamlessly!
Imagine having an ultra-intelligent "universal adhesive" that can perfectly bond together fragments of various shapes and materials. MMaDA adopts such a "universal adhesive" — the unified diffusion architecture. This architecture features shared probability formulas and modality-agnostic design, meaning it processes text, images, and other types of data without needing modality-specific components! This way, AI can seamlessly switch and handle different data types, greatly improving efficiency and coherence.
Mixed Long Chain-of-Thought (CoT) Fine-Tuning: Teaching AI to Think Deeply!
We know that large models can "think" partly through "chains of thought" (CoT). However, MMaDA takes it further with its "mixed long chain-of-thought" fine-tuning strategy. It carefully designs a cross-modal unified CoT format, forcing AI to align reasoning processes in both text and visual domains. The purpose is to enhance AI's ability to handle complex tasks before entering the final reinforcement learning phase, giving it a "cold start" training, akin to equipping it with a "martial arts manual" to master deep thinking skills before实战!
Unified Reinforcement Learning Algorithm UniGRPO: Reasoning and Generation Hand in Hand!
Thinking alone isn't enough; AI also needs "practice makes perfect!" MMaDA proposes a unified policy gradient reinforcement learning algorithm specifically designed for diffusion models — UniGRPO. By using diverse reward modeling, it cleverly unifies post-training for reasoning and generation tasks, ensuring continuous improvement in model performance. Previously, reasoning and generation may have required different training methods, but UniGRPO acts as an "all-around coach," guiding AI to excel in both "intellectual competitions" (reasoning) and "creative workshops" (generation).
MMaDA's "Achievements": Dominating Across the Board!
With these three "black technologies" in place, the MMaDA-8B model demonstrates remarkable generalization capabilities across various tests, truly excelling across multiple domains:
Text Reasoning: It surpasses LLAMA-3-7B and Qwen2-7B! This means MMaDA shows stronger "intelligence" than others in solving mathematical problems and logical reasoning in complex text tasks!
Multimodal Understanding: It outperforms Show-o and SEED-X! In understanding images and answering image-related questions, MMaDA provides more accurate and comprehensive responses.
Text-to-Image Generation: It surpasses SDXL and Janus! This is no small achievement; SDXL is currently recognized as a strong image generator, yet MMaDA generates more accurate and world-knowledge-consistent images, thanks to its powerful text reasoning capabilities!
AIbase believes that these achievements highlight the effectiveness of MMaDA in bridging the gap between "pre-training" and "post-training" in unified diffusion architectures, providing a comprehensive framework for future research and development.
Delving into MMaDA's "Internal Skills": How Does It Achieve "Seventy-Two Transformations"?
So, how does MMaDA achieve such "seventy-two transformations"?
Unified Tokenization: Whether it's text or images, MMaDA uses consistent discrete tokenization strategies. This turns all data into uniform "LEGO bricks," enabling the model to operate under a unified masked token prediction goal. For example, a 512x512 pixel image will be converted into 1024 discrete tokens! It’s like putting different modalities in the same "uniform"!
Three-Stage "Training Journey": MMaDA's training process is like "leveling up in a game," divided into three stages:
Base Pretraining (Stage1): Using massive amounts of text and multimodal data to lay a solid foundation for the model.
Mixed Long Chain-of-Thought Fine-Tuning (Stage2): Using meticulously planned "long chains of thought" data to teach the model reasoning and thinking. This step is crucial in moving the model from "knowing" to "understanding"!
UniGRPO Reinforcement Learning (Stage3): Finally, using reinforcement learning to continuously optimize the model in reasoning and generation tasks, striving for excellence.
Flexible Sampling Strategies: During inference, MMaDA is very flexible.
Text generation uses semi-autoregressive denoising strategies, producing more complex and detailed descriptions.
Image generation employs parallel non-autoregressive sampling, offering higher efficiency. This flexible combination ensures optimal performance across different tasks.
Not Just Generation: MMaDA Can Also "Fill in the Blanks"!
MMaDA has another hidden skill: it naturally supports image inpainting and extrapolation without additional fine-tuning! This is thanks to the characteristics of diffusion models, where these tasks can be viewed as "masked token prediction" problems, which are part of MMaDA's training goals!
This means:
It can predict missing parts of text sequences.
It can complete answers to visual question-answering given partial inputs and an image.
It can even repair images based on incomplete visual prompts!
This turns AI into a universal assistant capable of "imagining" visuals and "filling in the blanks," greatly expanding its application scenarios and generalization capabilities!
Conclusion: Is the Diffusion Model the New Paradigm for AI?
The birth of MMaDA is undoubtedly a milestone in the field of multi-modal AI. It systematically explores the design space of general foundational models based on diffusion models and proposes innovative post-training strategies. Experimental results show that MMaDA not only matches specialized models but even outperforms them in certain aspects, fully demonstrating the tremendous potential of diffusion models as the next-generation foundation paradigm for multi-modal intelligence!
Although MMaDA's current model size (8B parameters) still has room for improvement, its emergence undoubtedly outlines a grander and more unified future for the AI field. Imagine a future where AI is no longer a collection of individual "experts," but rather a "versatile genius" capable of deep thinking, cross-modal understanding, and endless creativity!
Project Address: https://github.com/Gen-Verse/MMaDA