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

Field	Value
Base model	LingBot-VA RoboTwin post-training checkpoint
Task	Joint future-video and robot-action prediction
Benchmark	RoboTwin 2.0
Released student	1 video step / 1 action step
Action dimension	30 in the released transformer config
Reported latency hardware	1 × NVIDIA L40S
Checkpoint license	Apache-2.0

Repository contents

Directory	Description
`transformer/`	Distilled Flash-WAM student, approximately 10 GB
`vae/`	VAE inherited from the LingBot-VA teacher, approximately 2.8 GB
`text_encoder/`	UMT5-XXL text encoder, approximately 11.3 GB
`tokenizer/`	T5 tokenizer files

The full snapshot is approximately 24 GB.

Download

bash

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:

bash

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:

Follow the LingBot-VA installation and RoboTwin evaluation instructions.
Clone the Flash-WAM repository so the custom wan_va model implementation is available.
Download this snapshot and set the model path in the LingBot-VA/Flash-WAM evaluation configuration to the local snapshot directory.
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:

python

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

Method	Video steps	Action steps	Average success	Speedup
LingBot-VA teacher	25	50	91.25%	1.0×
Naive joint LCM	1	2	23.97%	—
Flash-WAM	1	2	85.54%	19.0×
Naive joint LCM	1	1	36.32%	—
Flash-WAM	1	1	81.41%	23.3×

LIBERO

Method	Video steps	Action steps	Average success	Speedup
LingBot-VA teacher	20	50	98.6%	1.0×
Flash-WAM	1	2	95.7%	13.7×
Flash-WAM	1	1	95.1%	16.3×

Real-world Unitree G1

Three manipulation tasks were evaluated with 10 rollouts per task:

Method	Video/action steps	T1	T2	T3	Average
LingBot-VA	3 / 10	50%	70%	80%	66.7%
Flash-WAM	1 / 2	50%	60%	70%	60.0%
Flash-WAM	1 / 1	40%	50%	60%	50.0%

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

bibtex

@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}
}