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README.md

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+---
+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.