Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model

Instead of training a new model from scratch, Google DeepMind retrofitted Gemma 4 into a diffusion model using less than 10 percent of the original training budget. DiffusionGemma generates 256 tokens in parallel instead of one at a time, hitting about 1,500 tokens per second. Quality still trails the original autoregressive model in benchmarks, especially on reasoning tasks.

The article Google's DiffusionGemma proves you don't need to train from scratch to build a text diffusion model appeared first on The Decoder.