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DreamZero is a World Action Model that jointly predicts actions and videos, achieving strong zero-shot performance on unseen tasks. This release package contains everything needed to load a pretrained DreamZero model and run distributed inference via a WebSocket server.

Available Now

• Pretrained DreamZero-DROID model checkpoint
• Distributed WebSocket inference server (GB200, H100)
• DiT caching for optimized inference (~0.6s on GB200, ~3s on H100)
• DROID simulation evaluation support
• RoboArena integration (DROID real)
• Video generation and saving (MP4)
• New 02/16: LoRA training script for DreamZero, with preprocessed DROID dataset
• New 02/20: Full fine-tuning support

Coming Soon

• DreamZero training script on new embodiment (e.g. YAM)
• PolaRiS simulation environment support
• Genie 3.0 sim environment support

We provide an inference script that directly evaluates a hosted DreamZero-DROID policy on sim_evals. To test out the policy, first request access to the API via this form link. Then, follow these instructions to install sim_evals and launch evaluation.

# Clone repository
git clone --recurse-submodules https://github.com/arhanjain/sim-evals.git
cd sim-evals

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Activate uv environment
uv sync
source .venv/bin/activate

# [Optional] update pytorch versions
pip install torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cu129

# Download assets (may need to export HF_TOKEN=<YOUR_HUGGINGFACE_TOKEN> first)
uvx hf download owhan/DROID-sim-environments --repo-type dataset --local-dir assets

# Run eval script
cd ..
python eval_utils/run_sim_eval.py --host <API_HOST> --port <API_PORT> 

The outputs are saved in runs directory.

• Python: 3.11
• Hardware: Multi-GPU setup (tested on GB200, H100)
  • Minimum: 2 GPUs for distributed inference
• CUDA: Compatible GPU with CUDA 12.9+

1. Create conda environment:

conda create -n dreamzero python=3.11
conda activate dreamzero

2. Install dependencies (PyTorch 2.8+ with CUDA 12.9+):

pip install -e . --extra-index-url https://download.pytorch.org/whl/cu129

3. Install flash attention:

MAX_JOBS=8 pip install --no-build-isolation flash-attn

4. [GB200 ONLY, SKIP FOR H100] Install Transformer Engine:

pip install --no-build-isolation transformer_engine[pytorch]

We release a 14B pretrained DROID checkpoint on Huggingface. To download the checkpoint, run

hf download GEAR-Dreams/DreamZero-DROID --repo-type model --local-dir <path/to/checkpoint>

The inference server uses PyTorch distributed training utilities to parallelize the model across multiple GPUs:

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.run --standalone --nproc_per_node=2 socket_test_optimized_AR.py --port 5000 --enable-dit-cache --model-path <path/to/checkpoint>

To verify the server is working, run a test client. The first few inferences will take a few minutes to warm up. After warming up, inference takes ~0.6s on GB200 and ~3s on H100.

python test_client_AR.py --port 5000

• --port: Port number for the WebSocket server (default: 8000)
• --model-path: Path to the pretrained model checkpoint directory
• --enable-dit-cache: Enable caching in DiT layers for faster inference (recommended)
• --max-chunk-size: Override max_chunk_size for inference (optional)
• --timeout-seconds: Server timeout in seconds (default: 50000)
• --index: Index for output directory naming (default: 0)

The server saves:

• Videos: Generated video predictions as MP4 files in {model_path}/real_world_eval_gen_{date}_{index}/{checkpoint_name}/
• Input observations: Saved per message in {output_dir}/inputs/{msg_index}_{timestamp}/

DreamZero is built on top of Wan2.1-I2V-14B-480P and uses the umt5-xxl tokenizer. Download both before training:

pip install "huggingface_hub[cli]"

# You may need to set your HuggingFace token:
# export HF_TOKEN=<YOUR_HUGGINGFACE_TOKEN>

# Download Wan2.1 model weights (~28GB)
hf download Wan-AI/Wan2.1-I2V-14B-480P --local-dir ./checkpoints/Wan2.1-I2V-14B-480P

# Download umt5-xxl tokenizer
hf download google/umt5-xxl --local-dir ./checkpoints/umt5-xxl

Note: The training script will auto-download these if they are not found at the configured paths, but pre-downloading is recommended to avoid delays at launch.

We release the preprocessed DROID dataset used to train DreamZero on HuggingFace: GEAR-Dreams/DreamZero-DROID-Data.

This dataset is derived from the DROID 1.0.1 dataset with the following modifications:

• Converted from RLDS/TFDS format to LeRobot v2.0 format
• Idle frames removed using Physical Intelligence's idle frame detector (droid_sample_ranges_v1_0_1.json)
• Episodes without language annotations are filtered out
• Successful episodes only (episodes with non-zero reward)
• 3 camera views: exterior_image_1_left, exterior_image_2_left, wrist_image_left

To download the preprocessed dataset (~131GB):

huggingface-cli download GEAR-Dreams/DreamZero-DROID-Data --repo-type dataset --local-dir ./data/droid_lerobot

If you want to reproduce the dataset conversion from raw DROID 1.0.1 yourself (or modify the filtering), see docs/DROID_CONVERSION.md.

# Configure paths (override defaults as needed)
export DROID_DATA_ROOT="./data/droid_lerobot"
export OUTPUT_DIR="./checkpoints/dreamzero_droid"
export NUM_GPUS=4

# Point to your downloaded model weights (if not using default paths)
export WAN_CKPT_DIR="./checkpoints/Wan2.1-I2V-14B-480P"
export TOKENIZER_DIR="./checkpoints/umt5-xxl"

# Launch training
bash scripts/train/droid_training.sh

The training script uses Hydra for configuration and DeepSpeed ZeRO Stage 2 for distributed training. Key defaults:

Note: max_steps=10 is set for a quick sanity check. For full training, increase this to your desired number of steps and configure save_steps / save_strategy accordingly.

If you use DreamZero in your research, please cite:

@misc{ye2026worldactionmodelszeroshot,
      title={World Action Models are Zero-shot Policies}, 
      author={Seonghyeon Ye and Yunhao Ge and Kaiyuan Zheng and Shenyuan Gao and Sihyun Yu and George Kurian and Suneel Indupuru and You Liang Tan and Chuning Zhu and Jiannan Xiang and Ayaan Malik and Kyungmin Lee and William Liang and Nadun Ranawaka and Jiasheng Gu and Yinzhen Xu and Guanzhi Wang and Fengyuan Hu and Avnish Narayan and Johan Bjorck and Jing Wang and Gwanghyun Kim and Dantong Niu and Ruijie Zheng and Yuqi Xie and Jimmy Wu and Qi Wang and Ryan Julian and Danfei Xu and Yilun Du and Yevgen Chebotar and Scott Reed and Jan Kautz and Yuke Zhu and Linxi "Jim" Fan and Joel Jang},
      year={2026},
      eprint={2602.15922},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2602.15922}, 
}

This project is licensed under the Apache License 2.0.

For issues and questions:

• Check the troubleshooting section above
• Review server logs for detailed error messages
• Verify your checkpoint is compatible with this release