This research paper introduces Masked Diffusion Models (MDMs) as a strong alternative to the traditional Autoregressive Models (ARMs) for language modeling. MDMs predict missing words within a sentence, using information from all the other words, while ARMs predict words one by one, only using the preceding words in the sentence. The research demonstrates that MDMs are as efficient as ARMs and sometimes even better, particularly in understanding language and generating text. They are especially good at tasks that are challenging for ARMs, such as understanding relationships where the order of words matters (like understanding that “The cat chased the mouse” also means “The mouse was chased by the cat”) and adapting to changes in language use over time. The researchers believe that scaling up MDMs can make them even more powerful and competitive with the best language models available.