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# Diffusion Language Models (dLLMs)

Diffusion language models (dLLMs) generate text by **denoising** rather than left-to-right autoregression. A fixed-length response "canvas" is corrupted and then iteratively refined, so tokens are produced in parallel and can be revised across steps. NeMo AutoModel supports fine-tuning block-diffusion dLLMs with the same recipe-driven, FSDP2/Expert-Parallel training stack used for LLMs and VLMs.

## Supported Models

| Owner  | Model Family                                          | Architectures                     |
| ------ | ----------------------------------------------------- | --------------------------------- |
| Google | [DiffusionGemma](/model-coverage/dllm/diffusiongemma) | `DiffusionGemmaForBlockDiffusion` |

## Fine-Tuning

See the [DiffusionGemma Fine-Tuning Guide](/recipes-e2e-examples/diffusiongemma) for the block-diffusion training objective (uniform-random token corruption, no `[MASK]`), self-conditioning, and the supported feature set (SFT, LoRA, Expert Parallelism, activation checkpointing).