LLaDA2.0 mini operates as a diffusion-based language model rather than the typical autoregressive approach, generating text by iteratively refining masked tokens instead of predicting word by word. This makes its generation process feel fundamentally different under the hood. As a compact model, it trades raw capability for efficiency, suited to experimentation with non-autoregressive architectures.