Arxiv paper – MMaDA: Multimodal Large Diffusion Language Models

In this episode, we discuss MMaDA: Multimodal Large Diffusion Language Models by Ling Yang, Ye Tian, Bowen Li, Xinchen Zhang, Ke Shen, Yunhai Tong, Mengdi Wang. MMaDA is a unified multimodal diffusion foundation model that leverages a modality-agnostic architecture, a mixed long chain-of-thought fine-tuning strategy, and a novel unified policy-gradient reinforcement learning algorithm to excel across textual reasoning, multimodal understanding, and text-to-image generation. It achieves superior performance compared to leading models in each domain by bridging pretraining and post-training effectively within one framework. The model and code are open-sourced to support future research and development.