LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining masked tokens through a diffusion process rather than predicting the next token in a sequence.
The project functions as a vision-language diffusion model, converting visual inputs into text responses. It also serves as a preference optimization framework that uses log-likelihood estimation and evidence lower bounds to tune model responses.
The system supports multi-round conversational AI and text sequence evaluation. It integrates vision-language embedding for cross-modal conditioning and uses iterative token refinement to produce text.