| --- |
| license: apache-2.0 |
| library_name: diffusers |
| tags: |
| - robotics |
| - world-model |
| - diffusion |
| - step-distillation |
| - lingbot-va |
| pipeline_tag: robotics |
| --- |
| |
| # Flash-WAM — RoboTwin (distilled) |
|
|
| Single-step distilled checkpoint for **Flash-WAM: Modality-Aware Distillation for World Action Models**, applied to LingBot-VA and evaluated on RoboTwin 2.0. Flash-WAM distills each modality with a consistency function matched to its noise regime (linear-gradient-scaling for the action stream, variance-preserving for the video stream), compressing inference to a single step per modality for up to a **23× speedup** while preserving teacher-level task success. |
|
|
| This repository contains the **complete model** (distilled transformer + encoders): |
|
|
| | Component | Description | |
| | :--- | :--- | |
| | `transformer/` | Distilled Flash-WAM student | |
| | `vae/` | VAE (from the LingBot-VA teacher) | |
| | `text_encoder/` | UMT5-XXL text encoder (from the teacher) | |
| | `tokenizer/` | T5 tokenizer | |
|
|
| ## Links |
|
|
| - 📄 Paper: https://arxiv.org/abs/2606.05254 |
| - 🌐 Project page: https://flashwam.github.io |
| - 💻 Code: https://github.com/NU-World-Model-Embodied-AI/Flash-WAM |
|
|
| ## Usage |
|
|
| For environment setup and evaluation, follow the [Flash-WAM repository](https://github.com/NU-World-Model-Embodied-AI/Flash-WAM) and [LingBot-VA](https://github.com/Robbyant/lingbot-va). Point the inference server at this checkpoint directory. |
|
|
| ## 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}, |
| } |
| ``` |
|
|
| License: Apache-2.0. |
|
|
