diff --git a/lerobot/common/policies/dexvla/README.md b/lerobot/common/policies/dexvla/README.md
new file mode 100644
index 00000000..2f0d7786
--- /dev/null
+++ b/lerobot/common/policies/dexvla/README.md
@@ -0,0 +1,112 @@
+<h1 align="center">
+DexVLA: Vision-Language Model with Plug-In Diffusion Expert for Visuomotor Policy Learning</h1>
+
+### This is the lerobot version of DexVLA. For more information, you can refer to [this](https://github.com/juruobenruo/DexVLA).
+
+## Data Input
+DexVLA takes into RGB images, language instructions and states. For our setting, we use three camera views: a top camera, two wrist cameras. 
+
+⭐A major difference between DexVLA with other VLAs is: DexVLA takes raw language in, and outputs sub-step reasoning based on current observations and robot states.
+So you have to <font color='red'>add sub-step reasoning in your data for training</font>.
+
+Specifically, your data should include a key ``reasoning`` which is a list of sub-step reasoning corresponding to each observation.
+For example, if the episode is 10 steps. The length of this list should be 10 as well. And it may looks like:
+~~~python
+reasoning = [
+    "This is step 1.",
+    "This is step 1.",
+    "This is step 2.",
+    "This is step 2.",
+    ...
+    "This is step 4.",
+]
+~~~
+
+Besides, your data should include another key ``action_is_pad`` which is a bool mask indicated whether this action chunk is padded.
+For example, suppose action chunk is 5, and the length of episode is 10. So the action chunk for last 4 actions must be padded to make sure the length of action chunk is 5.
+And the mask looks like:
+~~~python
+The 6th chunk: [false, false, false, false, true] 
+The 7th chunk: [false, false, false, true,  true]
+The 8th chunk: [false, false, true,  true,  true]
+The 9th chunk: [false, true,  true,  true,  true]
+~~~
+
+## 🤗Download Pretrained Weights
+### Download official Qwen2_VL weights
+We construct the VLM backbone by integrating Qwen2-VL-2B, a powerful and efficient model, into our framework. 
+The Qwen2-VL 2B serves as the core of our architecture, providing robust capabilities 
+for vision-language tasks. We use off-the-shelf Qwen2-VL model proposed 
+in [Qwen2-VL](https://arxiv.org/pdf/2409.12191) without any post training on VLM itself. You can download the official weights from this link:
+
+| Model               | Link                                                           |
+|---------------------|----------------------------------------------------------------|
+| Qwen2-VL (~2B)      | [huggingface](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) |
+
+**❗❗** After downloading the standard weights, you have to replace the official "config.json"
+with our ["config.json"](https://github.com/juruobenruo/DexVLA/blob/main/docs/config.json) designed for VLA.
+### Download our pretrained ScaleDP-H weights(Stage 1)
+We released our pretrained weights of ScaleDP-H which is trained after Stage1. Now you can download the weights and directly finetuning your data on Stage 2.
+
+| Model             | Link                                                           |
+|-------------------|----------------------------------------------------------------|
+| ScaleDP-H (~1B)   | [huggingface](https://huggingface.co/lesjie/scale_dp_h)  |
+| ScaleDP-L (~400M) | [huggingface](https://huggingface.co/lesjie/scale_dp_l)  |
+
+## 🦾Train
+We have already provided pretrained weights of ScaleDP which is stage 1. Belows are mainly about training process of Stage2 and Stage3.
+
+### Training Stage 2
+~~~shell
+python lerobot/scripts/train.py \
+--policy.type dexvla \
+--policy.qwen2_vl_path /path/to/official/Qwen2-VL-2B-Instruct \
+--policy.pretrain_scaledp_path /path/to/pretrained/scale_dp_h/open_scale_dp_l_backbone.ckpt \
+--policy.policy_head_size 'ScaleDP_H' \
+--policy.training_stage 2 \
+--dataset.repo_i folding_blue_tshirt \
+--dataset.local_files_only true \
+--batch_size 2 \
+--policy.using_film true \
+--output_dir /path/to/output \
+--steps 10000 \
+--save_freq 1000 \
+--optimizer_lr 2e-5 \
+--policy.device=cuda
+~~~
+
+### Training Stage 3
+Stage3 can be viewed as continual training on specific dexterous tasks like laundry folding which is same as PI0. So stage3 is trained based on stage2.
+~~~shell
+python lerobot/scripts/train.py \
+--policy.type dexvla \
+--policy.qwen2_vl_path /path/to/official/Qwen2-VL-2B-Instruct \
+--.pretrained_path /path/to/pretrained/stage2/weights \
+--policy.policy_head_size 'ScaleDP_H' \
+--policy.training_stage 3 \
+--dataset.repo_i folding_blue_tshirt \
+--dataset.local_files_only true \
+--batch_size 2 \
+--policy.using_film true \
+--output_dir /path/to/output \
+--steps 10000 \
+--save_freq 1000 \
+--optimizer_lr 2e-5 \
+--policy.device=cuda
+~~~
+
+## Evaluation
+~~~shell
+python lerobot/scripts/eval.py \
+--policy.type dexvla \
+--policy.pretrained_path /path/to/pretrained/stage2/or/stage3/weights \
+--env.type aloha \
+--env.episode_length 5 \
+--policy.qwen2_vl_path /path/to/official/Qwen2-VL-2B-Instruct \
+--env.task AlohaInsertion-v0 \
+--eval.n_episodes 1 \
+--eval.batch_size 1 \
+--device cuda 
+~~~
+
+