BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation

• March 2026: 🚀 We have released the pre-trained BitVLA model! The evaluation results in the table below have been updated to reflect the performance after pre-training. You can try our new pre-trained model out-of-the-box.
• June 2025: BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation

Open Source Plan

• ✅ Paper, Pre-trained VLM and evaluation code.
• ✅ Fine-tuned VLA code and models
• ✅ Pre-trained VLA.
• 🧭 Pre-training code

Contents

• BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation
  • Contents
  • Checkpoints
  • Vision-Language
    • Evaluation on VQA
  • Vision-Language-Action
    • Robotics Pre-training
    • OFT Training
      • 1. Preparing OFT
      • 2. OFT fine-tuning
    • Evaluation on LIBERO
  • Acknowledgement
  • Citation
  • License
    • Contact Information

Checkpoints

Models	Size	Memory Usage↓	LIBERO-Spatial	LIBERO-Object	LIBERO-Goal	LIBERO-Long	Avg.
Large Models
OpenVLA	7.5B	15.1GB (10.79×)	84.7	88.4	79.2	53.7	76.5
CoT-VLA	8.0B	16.2GB (11.57×)	87.5	91.6	87.6	69.0	81.1
UniVLA	8.5B	17.0GB (12.14×)	96.5	96.8	95.6	92.0	95.2
UnifiedVLA	8.5B	17.0GB (12.14×)	95.4	98.8	93.6	94.0	95.5
OpenVLA-OFT	7.7B	15.4GB (11.00×)	97.6	98.4	97.9	94.5	97.1
Small Models
SpatialVLA	4.2B	8.5GB (6.07×)	88.2	89.9	78.6	55.5	78.1
NORA-Long	3.8B	7.5GB (5.36×)	92.2	95.4	89.4	74.6	87.9
4D-VLA	4.1B	8.3GB (5.93×)	88.9	95.2	90.9	79.1	88.6
SmolVLA	2.3B	4.6GB (3.29×)	93.0	94.0	91.0	77.0	88.8
GROOT-N1	2.2B	4.4GB (3.14×)	94.4	97.6	93.0	90.6	93.9
π₀	3.5B	7.0GB (5.00×)	96.8	98.8	95.8	85.2	94.2
BitVLA w/o pre-training	3.0B	1.4GB (1.00×)	97.4	99.6	94.4	87.6	94.8
🚀BitVLA	3.0B	1.4GB (1.00×)	96.6	99.0	95.4	92.8	96.0

Model	Path
🚀BitVLA - VL&VLA pre-trained	lxsy/bitvla-bf16
BitVLA - VL pre-trained	hongyuw/bitvla-bitsiglipL-224px-bf16
BitVLA finetuned on LIBERO-Spatial	hongyuw/ft-bitvla-bitsiglipL-224px-libero_spatial-bf16
BitVLA finetuned on LIBERO-Object	hongyuw/ft-bitvla-bitsiglipL-224px-libero_object-bf16
BitVLA finetuned on LIBERO-Goal	hongyuw/ft-bitvla-bitsiglipL-224px-libero_long-bf16
BitVLA finetuned on LIBERO-Long	hongyuw/ft-bitvla-bitsiglipL-224px-libero_long-bf16
BitVLA w/ BF16 SigLIP	hongyuw/bitvla-siglipL-224px-bf16

*Note that we provide the master weights of BitVLA and perform online quantization. For actual memory savings, you may quantize the weights offline to 1.58-bit precision. We recommend using the bitnet.cpp inference framework to accurately measure the reduction in inference cost. A dedicated inference framework and model are coming soon.

Vision-Language

Evaluation on VQA

We use the LMM-Eval toolkit to conduct evaluations on VQA tasks. We provide the transformers repo in which we modify the modeling_llava.py and modeling_siglip.py to support the W1.58-A8 quantization.

The evaluation should use nvidia_24_07 docker. Install the packages:

docker run --name nvidia_24_07  --privileged --net=host --ipc=host --gpus=all -v /mnt:/mnt -v /tmp:/tmp -d nvcr.io/nvidia/pytorch:24.07-py3 sleep infinity # only use for multimodal evaluation
docker exec -it nvidia_24_07 bash
git clone https://github.com/ustcwhy/BitVLA.git
cd BitVLA/
bash vl_eval_setup.sh # only use for multimodal evaluation

First, download the BitVLA model from HuggingFace:

git clone https://huggingface.co/hongyuw/bitvla-bitsiglipL-224px-bf16 # BitVLA w/ W1.58-A8 SigLIP-L
git clone https://huggingface.co/hongyuw/bitvla-siglipL-224px-bf16 # BitVLA w/ BF16 SigLIP-L

Then run the following scripts to conduct evaluations:

cd lmms-eval/
bash eval-dense-hf.sh /YOUR_PATH_TO_EXP/bitvla-bitsiglipL-224px-bf16
bash eval-dense-hf.sh /YOUR_PATH_TO_EXP/bitvla-siglipL-224px-bf16

Note that we provide the master weights of BitVLA and perform online quantization. For actual memory savings, you may quantize the weights offline to 1.58-bit precision. We recommend using the bitnet.cpp inference framework to accurately measure the reduction in inference cost.

Vision-Language-Action

Robotics Pre-training

To endow BitVLA with generalizable manipulation priors that transfer across embodiments and environments, we pre-train it with an autoregressive next-action prediction objective following OpenVLA.

Pre-training Details:

• Base model: We use hongyuw/bitvla-bitsiglipL-224px-bf16 as the base model.
• Dataset: Following OpenVLA, we use a curated large-scale corpus based on a subset of the Open X-Embodiment dataset, resulting in ~1M training samples.
• Hyperparameters: We train the model for 200K steps with a total batch size of 2048. The peak learning rates are set to 3×10⁻⁴ for the LLM and 1×10⁻⁴ for the ViT.
• Compute: The full pre-training takes approximately 14 days on 16 NVIDIA H800 (80GB) GPUs.

OFT Training

1. Preparing OFT

We fine-tune BitVLA using OFT training shown in OpenVLA-OFT. First setup the environment as required by that project. You can refer to SETUP.md and LIBERO.md for detailed instructions.

conda create -n bitvla python=3.10 -y
conda activate bitvla
pip install torch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 --index-url https://download.pytorch.org/whl/cu124

# or use the provided docker
# docker run --name nvidia_24_07  --privileged --net=host --ipc=host --gpus=all -v /mnt:/mnt -v /tmp:/tmp -d nvcr.io/nvidia/pytorch:24.07-py3 sleep infinity

cd BitVLA
pip install -e openvla-oft/
pip install -e transformers

cd openvla-oft/

# install LIBERO
git clone https://github.com/Lifelong-Robot-Learning/LIBERO.git
pip install -e LIBERO/
pip install -r experiments/robot/libero/libero_requirements.txt

# install bitvla
pip install -e bitvla/

We adopt the same dataset as OpenVLA-OFT for the fine-tuning on LIBERO. You can download the dataset from HuggingFace.

git clone git@hf.co:datasets/openvla/modified_libero_rlds

2. OFT fine-tuning

Prepare the BitVLA

• 🚀 New pre-trained model (Recommended): This model is ready to use out-of-the-box. No additional processing is required, and you can directly execute our provided scripts.
• 🕰️ Old model: This version was not pre-trained on the Open X-Embodiment dataset. To use this model, you must first convert the model into a format compatible with our codebase before using it.

  python convert_ckpt.py /path/to/bitvla-bitsiglipL-224px-bf16
  

Fine-tuing the BitVLA

After that, you can finetune BitVLA using the provided shell script.

sh ft_script/ft_bitvla_libero_spatial.sh
sh ft_script/ft_bitvla_libero_object.sh
sh ft_script/ft_bitvla_libero_goal.sh
sh ft_script/ft_bitvla_libero_long.sh

Evaluation on LIBERO

You can download our fine-tuned BitVLA models from HuggingFace. As an example for spatial set in LIBERO, run the following script for evaluation:

python experiments/robot/libero/run_libero_eval_bitnet.py \
    --pretrained_checkpoint  /path/to/ft-bitvla-bitsiglipL-224px-libero_spatial-bf16 \
    --task_suite_name libero_spatial \
    --info_in_path "information you want to show in path" \
    --model_family "bitnet" 

Acknowledgement

This repository is built using LMM-Eval, the HuggingFace's transformers, OpenVLA-OFT and OpenVLA.

Citation

If you find this repository useful, please consider citing our work:

@article{bitvla,
  title={BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation}, 
  author={Hongyu Wang and Chuyan Xiong and Ruiping Wang and Xilin Chen},
  year={2025},
  eprint={2506.07530},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
}

License

This project is licensed under the MIT License.

Contact Information

For help or issues using models, please submit a GitHub issue.