🎉 Good News! Our paper has been accepted to ICML 2026!

This is a PyTorch implementation of the paper: VLANeXt: Recipes for Building Strong VLA Models, and also a unified, easy-to-use codebase that standardizes training and evaluation while exposing the key components of the VLA design space. It is intentionally lightweight and minimally encapsulated, enabling researchers to reproduce results, probe alternative design choices, and build new VLA variants on a shared, transparent foundation. We also release a curated and continuously updated list of VLA & WAM research (Awesome VLA & WAM) to help better understand the development of VLAs.

Xiao-Ming Wu, Bin Fan, Kang Liao, Jian-Jian Jiang, Runze Yang, Yihang Luo, Zhonghua Wu, Wei-Shi Zheng, Chen Change Loy*.

S-Lab, Nanyang Technological University; Sun Yat-sen University; ACE Robotics.

Let's build the future of VLAs together! If you have any questions, feel free to contact me by xiaoming.wu@ntu.edu.sg.

# Basic setup 
conda create -n VLANeXt python=3.10
conda activate VLANeXt
pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
conda install -c conda-forge ffmpeg

LIBERO

cd third_party
git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git
cd LIBERO && pip install .

LIBERO-plus (Separate env needed)

cd third_party
git clone https://github.com/sylvestf/LIBERO-plus.git
cd LIBERO-plus && pip install .
# Dependencies
apt install libexpat1 libfontconfig1-dev libpython3-stdlib libmagickwand-dev
pip install -r extra_requirements.txt
conda env config vars set LIBERO_CONFIG_PATH=~/.libero_plus

We also need to download the asserts, see LIBERO-plus.

Droid dataset is for robotics pretraining (used in our real-world experiments), and libero dataset is for benchmark evaluation (used in our benchmark evaluation).

We provide a tutorial-style guide to configuring the 12 design spaces from our paper.

👉 Please refer to DESIGN_SPACE.md for detailed configuration instructions.

For more details, please refer to the Droid Dataset.

Download:

gsutil -m rsync -r gs://gresearch/robotics/droid/1.0.1 droid/1.0.1/ 

Run Training:

# Single GPU
CUDA_VISIBLE_DEVICES=0 python -m scripts.train --config config/droid_train_config.yaml

# Multi-GPU (Set distributed=true in config)
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 --master_port=29505 -m scripts.train --config config/droid_train_config.yaml

For more details, please refer to the OpenVLA, which modifies the original dataset in LIBERO for training VLAs.

Download:

hf download openvla/modified_libero_rlds --repo-type dataset --local-dir LIBERO_modified

Run Training:

# Single GPU
CUDA_VISIBLE_DEVICES=0 python -m scripts.train --config config/libero_train_config.yaml

# Multi-GPU (Set distributed=true in config)
CUDA_VISIBLE_DEVICES=4,5,6,7 torchrun --nproc_per_node=4 --master_port=29506 -m scripts.train --config config/libero_train_config.yaml

We have released VLANeXt checkpoints for the four LIBERO or LIBERO-plus suites on huggingface.

For more details, please refer to the official repository of LIBERO.

unset PYTHONPATH
export PYTHONPATH=$PYTHONPATH:/data/NTU_slab/draven/proj/third_party/LIBERO

CUDA_VISIBLE_DEVICES=0 MUJOCO_EGL_DEVICE_ID=0 python -m scripts.libero_bench_eval

For more details, please refer to the official repository of LIBERO-plus.

unset PYTHONPATH
export PYTHONPATH=$PYTHONPATH:/data/NTU_slab/draven/proj/third_party/LIBERO-plus

CUDA_VISIBLE_DEVICES=0 MUJOCO_EGL_DEVICE_ID=0 python -m scripts.libero_plus_bench_eval

Model Size and Speed
Set CHECKPOINT_PATH and INPUT_MODALITY in scripts/size_speed_eval.py.

CUDA_VISIBLE_DEVICES=0 python -m scripts.size_speed_eval

If you run into issues, check COMMON_ISSUES.md for known problems and solutions.

If you find VLANeXt useful for your research or applications, please cite our paper using the following BibTeX:

  @article{wu2026vlanext,
      title={VLANeXt: Recipes for Building Strong VLA Models}, 
      author={Xiao-Ming Wu and Bin Fan and Kang Liao and Jian-Jian Jiang and Runze Yang and Yihang Luo and Zhonghua Wu and Wei-Shi Zheng and Chen Change Loy},
      journal={arXiv preprint arXiv:2602.18532},
      year={2026},
  }

This project is licensed under NTU S-Lab License 1.0.