Flash-WAM RoboTwin: Distilled World-Action Model

Project page · Paper · Code · LingBot-VA

This repository contains the complete RoboTwin checkpoint for Flash-WAM: Modality-Aware Distillation for World Action Models. Flash-WAM distills the joint video and action streams of LingBot-VA with consistency functions matched to their different noise regimes.

The released student supports one-step video and one-step action generation. Under the paper's RoboTwin 2.0 setup on a single NVIDIA L40S, this reduces per-chunk latency from 8.1 seconds to 348 milliseconds, a 23.3× speedup.

Important: this is a custom joint video-action robotics model, not a generic text-to-image or video DiffusionPipeline. Do not use DiffusionPipeline.from_pretrained(...). Install the Flash-WAM/LingBot-VA code and use their RoboTwin server/client evaluation path.

Model details

Repository contents

The full snapshot is approximately 24 GB.

Download

pip install -U huggingface_hub

hf download NU-World-Model-Embodied-AI/FlashWAM-RoboTwin \
  --local-dir ./FlashWAM-RoboTwin

To inspect configs without downloading the weights:

hf download NU-World-Model-Embodied-AI/FlashWAM-RoboTwin \
  README.md \
  transformer/config.json vae/config.json text_encoder/config.json \
  tokenizer/tokenizer_config.json \
  --local-dir ./FlashWAM-RoboTwin-config

Environment and evaluation

Flash-WAM uses the LingBot-VA environment and the same RoboTwin server/client evaluation pipeline:

1. Follow the LingBot-VA installation and RoboTwin evaluation instructions.
2. Clone the Flash-WAM repository so the custom wan_va model implementation is available.
3. Download this snapshot and set the model path in the LingBot-VA/Flash-WAM evaluation configuration to the local snapshot directory.
4. For training or distillation, use the released commands in the Flash-WAM repository; this checkpoint is the already-distilled student.

The public Flash-WAM repository does not yet include the real-world Unitree G1 deployment setup. Do not infer a supported real-robot deployment command from the checkpoint layout alone.

Optional component-loading check

After installing the LingBot-VA environment and making the Flash-WAM repository available on PYTHONPATH, the released helper functions can load the individual components:

from pathlib import Path
import torch

from wan_va.modules.utils import (
    load_text_encoder,
    load_tokenizer,
    load_transformer,
    load_vae,
)

root = Path("/path/to/FlashWAM-RoboTwin")
device = "cuda"
dtype = torch.bfloat16

tokenizer = load_tokenizer(root / "tokenizer")
text_encoder = load_text_encoder(root / "text_encoder", dtype, device)
vae = load_vae(root / "vae", dtype, device)
transformer = load_transformer(root / "transformer", dtype, device)

This verifies component compatibility; it is not a complete policy rollout. Use the upstream server/client evaluation path for observations, action normalization, temporal caching, and environment interaction.

Reported results

RoboTwin 2.0

LIBERO

Real-world Unitree G1

Three manipulation tasks were evaluated with 10 rollouts per task:

The fastest 1-video/1-action-step configuration and the 60% real-world result are not the same configuration. Report step budgets together with every success-rate or latency claim.

Intended use

This checkpoint is intended for:

• research on step distillation for joint video-action models;
• reproducing the reported RoboTwin results;
• comparing modality-aware and naive joint consistency objectives;
• studying latency/task-success trade-offs in world-action models.

It is not a drop-in controller for an arbitrary robot or task. Deployment on physical robots requires task-specific observation processing, action normalization, safety constraints, control integration, and validation.

Limitations and safety

• Results are specific to LingBot-VA, the released RoboTwin checkpoint, and the paper's evaluation settings.
• Latency depends on GPU, software stack, precision, resolution, horizon, and server/client overhead; 348 ms is not a universal runtime guarantee.
• The real-world evaluation covers three tasks and 30 rollouts per method.
• One-step generation still reduces task success relative to the teacher.
• Generated actions may be unsafe or incorrect. Use independent safeguards, workspace limits, emergency stops, and supervised testing before any physical deployment.
• The real-world G1 deployment setup is not included in the public code release.

Licenses

The checkpoint in this Hugging Face repository is released under Apache-2.0. It includes components derived from LingBot-VA, whose released model and bundled upstream components are also Apache-2.0.

The separate Flash-WAM GitHub repository uses different terms for different parts: the authors' distillation code, documentation, and demo videos are CC BY-NC 4.0, while the bundled wan_va/ components remain Apache-2.0. Downloading this checkpoint does not replace the license notices of the code or other assets used with it.

Citation

@misc{akbari2026flashwammodalityawaredistillationworld,
  title         = {Flash-WAM: Modality-Aware Distillation for World Action Models},
  author        = {Arman Akbari and Ci Zhang and Arash Akbari and Lin Zhao and Yixiao Chen and Weiwei Chen and Xuan Zhang and Geng Yuan and Yanzhi Wang},
  year          = {2026},
  eprint        = {2606.05254},
  archivePrefix = {arXiv},
  primaryClass  = {cs.LG},
  url           = {https://arxiv.org/abs/2606.05254}
}