The Sequence AI of the Week #878: Inside Google Deepmind's First Real Crack in Next-Token Generation
Google DeepMind has released DiffusionGemma, a text-diffusion model that challenges traditional transformer architectures by not generating text left-to-right token by token.
As we wrap up our series about alternatives to transformer architectures, Google DeepMind just released one of the most impressive models in this category. DiffusionGemma is a text-diffusion model that challenges the conventional transfromer models. Today, we would like to deep dive into the specifics of this model.
Most language models write like a typewriter. They place one token after another, left to right, never revisiting the characters already stamped onto the page. This architecture has carried the entire modern LLM era: GPT-style chatbots, coding copilots, reasoning models, agent frameworks, enterprise assistants. The model predicts the next token, appends it, updates its state, and repeats.
Google’s new DiffusionGemma asks a deceptively simple question: what if text generation did not have to work that way?
Let’s dive in.
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