
# LeWorldModel
### Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

[Lucas Maes*](https://x.com/lucasmaes_), [Quentin Le Lidec*](https://quentinll.github.io/), [Damien Scieur](https://scholar.google.com/citations?user=hNscQzgAAAAJ&hl=fr), [Yann LeCun](https://yann.lecun.com/) and [Randall Balestriero](https://randallbalestriero.github.io/)

**Abstract:** Joint Embedding Predictive Architectures (JEPAs) offer a compelling framework for learning world models in compact latent spaces, yet existing methods remain fragile, relying on complex multi-term losses, exponential moving averages, pretrained encoders, or auxiliary supervision to avoid representation collapse. In this work, we introduce LeWorldModel (LeWM), the first JEPA that trains stably end-to-end from raw pixels using only two loss terms: a next-embedding prediction loss and a regularizer enforcing Gaussian-distributed latent embeddings. This reduces tunable loss hyperparameters from six to one compared to the only existing end-to-end alternative. With ~15M parameters trainable on a single GPU in a few hours, LeWM plans up to 48× faster than foundation-model-based world models while remaining competitive across diverse 2D and 3D control tasks. Beyond control, we show that LeWM's latent space encodes meaningful physical structure through probing of physical quantities. Surprise evaluation confirms that the model reliably detects physically implausible events.

<p align="center">
   <b>[ <a href="https://arxiv.org/pdf/2603.19312v1">Paper</a> | <a href="https://drive.google.com/drive/folders/1r31os0d4-rR0mdHc7OlY_e5nh3XT4r4e?usp=sharing">Checkpoints</a> | <a href="https://huggingface.co/collections/quentinll/lewm">Data</a> | <a href="https://le-wm.github.io/">Website</a> ]</b>
</p>

<br>

<p align="center">
  <img src="assets/lewm.gif" width="80%">
</p>

If you find this code useful, please reference it in your paper:
```
@article{maes_lelidec2026lewm,
  title={LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels},
  author={Maes, Lucas and Le Lidec, Quentin and Scieur, Damien and LeCun, Yann and Balestriero, Randall},
  journal={arXiv preprint},
  year={2026}
}
```

## Using the code
This codebase builds on [stable-worldmodel](https://github.com/galilai-group/stable-worldmodel) for environment management, planning, and evaluation, and [stable-pretraining](https://github.com/galilai-group/stable-pretraining) for training. Together they reduce this repository to its core contribution: the model architecture and training objective.

**Installation:**
```bash
uv venv --python=3.10
source .venv/bin/activate
uv pip install stable-worldmodel[train,env]
```

## Data

Datasets use the HDF5 format for fast loading. Download the data from [HuggingFace](https://huggingface.co/collections/quentinll/lewm) and decompress with:

```bash
tar --zstd -xvf archive.tar.zst
```

Place the extracted `.h5` files under `$STABLEWM_HOME` (defaults to `~/.stable-wm/`). You can override this path:
```bash
export STABLEWM_HOME=/path/to/your/storage
```

Dataset names are specified without the `.h5` extension. For example, `config/train/data/pusht.yaml` references `pusht_expert_train`, which resolves to `$STABLEWM_HOME/pusht_expert_train.h5`.

## Training

`jepa.py` contains the PyTorch implementation of LeWM. Training is configured via [Hydra](https://hydra.cc/) config files under `config/train/`.

Before training, set your WandB `entity` and `project` in `config/train/lewm.yaml`:
```yaml
wandb:
  config:
    entity: your_entity
    project: your_project
```

To launch training:
```bash
python train.py data=pusht
```

Checkpoints are saved to `$STABLEWM_HOME` upon completion.

For baseline scripts, see the stable-worldmodel [scripts](https://github.com/galilai-group/stable-worldmodel/tree/main/scripts/train) folder.

## Planning

Evaluation configs live under `config/eval/`. Set the `policy` field to the checkpoint path **relative to `$STABLEWM_HOME`**, without the `_object.ckpt` suffix:

```bash
# ✓ correct
python eval.py --config-name=pusht.yaml policy=pusht/lewm

# ✗ incorrect
python eval.py --config-name=pusht.yaml policy=pusht/lewm_object.ckpt
```

## Profiling

`eval.py` now supports optional inference profiling with PyTorch's native profiler.

Example:

```bash
python eval.py --config-name=pusht.yaml policy=pusht/lewm \
  inference_precision=bf16 \
  +profile.enabled=true \
  +profile.with_stack=true \
  +profile.record_shapes=true \
  +profile.profile_memory=true
```

Supported inference precision modes:
- `inference_precision=fp32`
- `inference_precision=bf16`
- `inference_precision=fp16`

Outputs are written next to the evaluation results:
- `torch_profile/key_averages.txt` for the aggregated operator table
- `torch_profile/trace.json` for Chrome tracing
- TensorBoard trace files under `torch_profile/`

The trace includes custom regions such as `eval.world_evaluate_from_dataset`, `lewm.get_cost`, `lewm.rollout`, and `lewm.predict` to make the planning path easier to inspect.

## Pretrained Checkpoints

Pre-trained checkpoints are available on [Google Drive](https://drive.google.com/drive/folders/1r31os0d4-rR0mdHc7OlY_e5nh3XT4r4e). Download the checkpoint archive and place the extracted files under `$STABLEWM_HOME/`.

<div align="center">

| Method | two-room | pusht | cube | reacher |
|:---:|:---:|:---:|:---:|:---:|
| pldm | ✓ | ✓ | ✓ | ✓ |
| lejepa | ✓ | ✓ | ✓ | ✓ |
| ivl | ✓ | ✓ | ✓ | — |
| iql | ✓ | ✓ | ✓ | — |
| gcbc | ✓ | ✓ | ✓ | — |
| dinowm | ✓ | ✓ | — | — |
| dinowm_noprop | ✓ | ✓ | ✓ | ✓ |

</div>

## Loading a checkpoint

Each tar archive contains two files per checkpoint:
- `<name>_object.ckpt` — a serialized Python object for convenient loading; this is what `eval.py` and the `stable_worldmodel` API use
- `<name>_weight.ckpt` — a weights-only checkpoint (`state_dict`) for cases where you want to load weights into your own model instance

To load the object checkpoint via the `stable_worldmodel` API:

```python
import stable_worldmodel as swm

# Load the cost model (for MPC)
cost = swm.policy.AutoCostModel('pusht/lewm')
```

This function accepts:
- `run_name` — checkpoint path **relative to `$STABLEWM_HOME`**, without the `_object.ckpt` suffix
- `cache_dir` — optional override for the checkpoint root (defaults to `$STABLEWM_HOME`)

The returned module is in `eval` mode with its PyTorch weights accessible via `.state_dict()`.

## Contact & Contributions
Feel free to open [issues](https://github.com/lucas-maes/le-wm/issues)! For questions or collaborations, please contact `lucas.maes@mila.quebec`
