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README.md

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 # RDT2-VQ: Vision-Language-Action with Residual VQ Action Tokens
 
-**RDT2-VQ** is an autoregressive Vision-Language-Action (VLA) model adapted from **\[Qwen2.5-VL-7B-Instruct]** and trained on large-scale **UMI** bimanual manipulation data.
+**RDT2-VQ** is an autoregressive Vision-Language-Action (VLA) model adapted from **[Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)** and trained on large-scale **UMI** bimanual manipulation data.
 It predicts a short-horizon **relative action chunk** (24 steps, 20 dims/step) from binocular wrist-camera RGB and a natural-language instruction.
 Actions are discretized with a lightweight **Residual VQ (RVQ)** tokenizer, enabling robust zero-shot transfer across **unseen embodiments** for simple, open-vocabulary skills (e.g., pick, place, shake, wipe).