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# Diffusion Language Models (dLLMs)

Diffusion language models (dLLMs) generate text by **denoising** rather than by using 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 and expert parallel training stack used for LLMs and VLMs.

## Supported Models

LLaDA, LLaDA2, Nemotron-Labs-Diffusion, and DFlash load through their Hugging Face remote-code implementations. DiffusionGemma has a native NeMo AutoModel implementation, and I-DLM converts a supported autoregressive checkpoint with a diffusion training objective.

This table combines recipe-backed checkpoints with documented model families. Dates show when the current checkpoint first appeared in a recipe, or when a documentation-only family page was added. See the [combined model support log](/nemo/automodel/v0.7/model-coverage/release-log) for recipe-backed checkpoints of every model type.

## Fine-Tuning

The [dLLM Fine-Tuning Guide](/nemo/automodel/v0.7/recipes-e2e-examples/dllm-fine-tuning) provides the complete recipe walkthrough for all four families. The [DiffusionGemma Fine-Tuning Guide](/nemo/automodel/v0.7/recipes-e2e-examples/diffusiongemma) describes the block-diffusion training objective (uniform random token corruption without `[MASK]`), self-conditioning, and the supported feature set (SFT, LoRA, expert parallelism, and activation checkpointing).