> This page is for version Nightly (default).
> For other versions, use one of these documentation indexes:
> - Nightly (default): https://docs.nvidia.com/nemo/automodel/nightly/llms.txt
> - Latest: https://docs.nvidia.com/nemo/automodel/latest/llms.txt
> - 0.7.0 · 26.10: https://docs.nvidia.com/nemo/automodel/v0.7/llms.txt
> - 0.5.0 · 26.06: https://docs.nvidia.com/nemo/automodel/v0.5/llms.txt
> - 0.4.0 · 26.04: https://docs.nvidia.com/nemo/automodel/v0.4/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.

# World Action Models

World action models are robot policies built on video generation: a video diffusion transformer predicts the next frames of the
robot's cameras from the task description and the current observation, and an action expert turns those predictions into the next
chunk of actions. Fine-tuning teaches both halves at once: the trunk learns the new scene and controller, the action expert learns
the new action space.

## Run World Action Models with NeMo AutoModel

Use a source checkout of NeMo AutoModel so that the package and its example recipes stay on one revision:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
uv sync --locked --all-groups --all-extras
```

For other installation options, see our [NeMo AutoModel Installation Guide](/nemo/automodel/get-started/installation).

> **Note**
>
> The recipes load the frozen encoders of each model (for Praxis-1, the Wan2.2 VAE and the Qwen3.5 text encoder) through the model
> builder's reference package, which must be on the Python path. The model cards below name it.

## Supported Models

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/model-coverage/release-log) for recipe-backed checkpoints of every model type.

| Date       | Model                                                         | Architectures | Recipe                                                                                                |
| :--------- | :------------------------------------------------------------ | :------------ | :---------------------------------------------------------------------------------------------------- |
| 2026-11-11 | [Praxis-1-droid](/nemo/automodel/model-coverage/wam/runwayml/praxis-1-droid) |               | [recipe](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/wam/praxis/praxis_1_libero.yaml) |

## Fine-Tune World Action Models

The checked-in recipes fine-tune all weights with FSDP2 and expert parallelism on one 8-GPU node, or train LoRA
adapters on the trunk and the action expert. The [Praxis-1 LIBERO tutorial](https://github.com/NVIDIA-NeMo/Automodel/blob/main/tutorials/praxis-1-libero/README.md) walks
through converting a benchmark's demonstrations, training, and evaluating before and after.