Update README.md

README.md

@@ -21,6 +21,7 @@ VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model t
 - 💬 Project page: [https://vla-adapter.github.io/](https://vla-adapter.github.io/)
 - 🖥️ Dataset: [https://huggingface.co/datasets/openvla/modified_libero_rlds/tree/main](https://huggingface.co/datasets/openvla/modified_libero_rlds/tree/main)
 - 🤗 HuggingFace: [https://huggingface.co/VLA-Adapter](https://huggingface.co/VLA-Adapter)
+- Github: [https://github.com/OpenHelix-Team/VLA-Adapter](https://github.com/OpenHelix-Team/VLA-Adapter)
 
 ## Model Details
 We have developed and released the VLA-Adapter family of VLA models, a series of fine-tuned generative 
@@ -43,201 +44,134 @@ This resulted in a high-performance VLA model on a tiny-scale backbone.
 ### Success Rate Comparison
 <table>
   <tr>
-  <td><strong>Category</strong>
-   </td>
-   <td><strong>Methods</strong>
-   </td>
-   <td><strong>Scale</strong>
-   </td>
-   <td><strong>LIBERO-Spatial</strong>
-   </td>
-   <td><strong>LIBERO-Object</strong>
-   </td>
-   <td><strong>LIBERO-Goal</strong>
-   </td>
-   <td><strong>LIBERO-Long</strong>
-   </td>
-  <td><strong>Avg.</strong>
-   </td>
-  </tr>
-  <tr>
-    <td rowspan="11">Large-scale</td>
-   <td>FlowVLA (Zhong et al., 2025)</td>
-   <td>8.5B</td><td>93.2</td><td>95.0</td><td>91.6</td><td>72.6</td><td>88.1</td>
+   <td><strong>LIBERO</strong></td>  <td><strong>Methods</strong></td>
+   <td><strong>Scale</strong></td>  <td><strong>Spatial</strong></td>
+   <td><strong>Object</strong></td>  <td><strong>Goal</strong></td>
+   <td><strong>Long</strong></td>  <td><strong>Avg.</strong></td>
   </tr>
 
-  <tr>
-   <td>UnifiedVLA (Wang et al., 2025)</td>
-   <td>8.5B</td><td>95.4</td><td> <i><u>98.8*</u></i></td><td> 93.6 </td><td>94.0 </td><td>95.5</td>
-  </tr>
+  <tr><td rowspan="10">Large-scale</td><td>FlowVLA (Zhong et al., 2025)</td>
+   <td>8.5B</td><td>93.2</td><td>95.0</td><td>91.6</td><td>72.6</td><td>88.1</td></tr>
 
-  <tr>
-   <td>OpenVLA (Kim et al., 2024)</td>
-   <td>7B</td><td>84.7</td><td>88.4</td><td>79.2</td><td>53.7</td><td>76.5</td>
-  </tr>
+  <tr><td>UnifiedVLA (Wang et al., 2025)</td>
+   <td>8.5B</td><td>95.4</td><td><i><u>98.8*</u></i></td><td> 93.6 </td><td>94.0 </td><td>95.5</td></tr>
 
-  <tr>
-   <td>OpenVLA-OFT (Kim et al., 2025)</td>
-   <td>7B</td><td><i><u>97.6*</u></i></td><td>98.4</td><td><b>97.9</b></td><td><i><u>94.5*</u></i></td><td><i><u>97.1*</u></i></td>
-  </tr>
+  <tr><td>OpenVLA (Kim et al., 2024)</td>
+   <td>7B</td><td>84.7</td><td>88.4</td><td>79.2</td><td>53.7</td><td>76.5</td></tr>
 
-  <tr>
-   <td>UniVLA (Bu et al., 2025)</td>
-   <td>7B</td><td>96.5</td><td> 96.8</td><td> 95.6 </td><td>92.0 </td><td>95.2</td>
-  </tr>
+  <tr><td>OpenVLA-OFT (Kim et al., 2025)</td>
+   <td>7B</td><td><i><u>97.6*</u></i></td><td>98.4</td><td><b>97.9</b></td><td><i><u>94.5*</u></i></td><td><i><u>97.1*</u></i></td></tr>
 
-  <tr>
-   <td>CoT-VLA (Zhao et al., 2025)</td>
-   <td>7B</td><td>87.5 </td><td>91.6 </td><td>87.6</td><td> 69.0</td><td> 81.1</td>
-  </tr>
+  <tr><td>UniVLA (Bu et al., 2025)</td>
+   <td>7B</td><td>96.5</td><td> 96.8</td><td> 95.6 </td><td>92.0 </td><td>95.2</td></tr>
 
-  <tr>
-   <td>WorldVLA (Cen et al., 2025)</td>
-   <td>7B</td><td>87.6</td><td> 96.2</td><td> 83.4</td><td> 60.0</td><td> 81.8</td>
-  </tr>
+  <tr><td>CoT-VLA (Zhao et al., 2025)</td>
+   <td>7B</td><td>87.5 </td><td>91.6 </td><td>87.6</td><td> 69.0</td><td> 81.1</td></tr>
 
-  <tr>
-   <td>TraceVLA (Zheng et al., 2025)</td>
-   <td>7B</td><td>84.6</td><td> 85.2</td><td> 75.1</td><td> 54.1</td><td> 74.8</td>
-  </tr>
+  <tr><td>WorldVLA (Cen et al., 2025)</td>
+   <td>7B</td><td>87.6</td><td> 96.2</td><td> 83.4</td><td> 60.0</td><td> 81.8</td></tr>
 
-  <tr>
-   <td>MolmoAct (Lee et al., 2025)</td>
-   <td>7B</td><td>87.0</td><td> 95.4 </td><td>87.6</td><td> 77.2 </td><td>86.6</td>
-  </tr>
+  <tr><td>TraceVLA (Zheng et al., 2025)</td>
+   <td>7B</td><td>84.6</td><td> 85.2</td><td> 75.1</td><td> 54.1</td><td> 74.8</td></tr>
 
-  <tr>
-   <td>ThinkAct (Huang et al., 2025)</td>
-   <td>7B</td><td>88.3 </td><td>91.4</td><td> 87.1</td><td> 70.9</td><td> 84.4</td>
-  </tr>
+  <tr><td>MolmoAct (Lee et al., 2025)</td>
+   <td>7B</td><td>87.0</td><td> 95.4 </td><td>87.6</td><td> 77.2 </td><td>86.6</td></tr>
 
-  <tr>
-   <td>PD-VLA (Song et al., 2025b)</td>
-   <td>7B</td><td>95.5 </td><td>96.7</td><td> 94.9</td><td> 91.7</td><td> 94.7</td>
-  </tr>
+  <tr><td>ThinkAct (Huang et al., 2025)</td>
+   <td>7B</td><td>88.3 </td><td>91.4</td><td> 87.1</td><td> 70.9</td><td> 84.4</td></tr>
 
-  <tr>
-  <td rowspan="8">Small-scale</td>
-   <td>4D-VLA (Zhang et al., 2025)</td>
-   <td>4B</td><td>88.9</td><td> 95.2</td><td> 90.9</td><td> 79.1 </td><td>88.6</td>
-  </tr>
+  <tr><td rowspan="7">Small-scale</td><td>4D-VLA (Zhang et al., 2025)</td>
+   <td>4B</td><td>88.9</td><td> 95.2</td><td> 90.9</td><td> 79.1 </td><td>88.6</td></tr>
 
-  <tr>
-   <td>SpatialVLA (Qu et al., 2025)</td>
-   <td>4B</td><td>88.2</td><td> 89.9</td><td> 78.6</td><td> 55.5 </td><td>78.1</td>
-  </tr>
-
-  <tr>
-   <td>π0 (Black et al., 2025)</td>
-   <td>3B</td><td>96.8</td><td> <i><u>98.8*</u></i> </td><td>95.8</td><td> 85.2</td><td> 94.2</td>
-  </tr>
+  <tr><td>SpatialVLA (Qu et al., 2025)</td>
+   <td>4B</td><td>88.2</td><td> 89.9</td><td> 78.6</td><td> 55.5 </td><td>78.1</td></tr>
 
-  <tr>
-   <td>π0-FAST (Pertsch et al., 2025)</td>
-   <td>3B</td><td>96.4</td><td> 96.8 </td><td>88.6</td><td> 60.2</td><td> 85.5</td>
-  </tr>
+  <tr><td>π0 (Black et al., 2024)</td>
+   <td>3B</td><td>96.8</td><td><i><u>98.8*</u></i></td><td>95.8</td><td> 85.2</td><td> 94.2</td></tr>
 
-  <tr>
-   <td>NORA (Hung et al., 2025)</td>
-   <td>3B</td><td>92.2 </td><td>95.4 </td><td>89.4</td><td> 74.6 </td><td>87.9</td>
-  </tr>
+  <tr><td>π0-FAST (Pertsch et al., 2025)</td>
+   <td>3B</td><td>96.4</td><td> 96.8 </td><td>88.6</td><td> 60.2</td><td> 85.5</td></tr>
 
-  <tr>
-   <td>SmolVLA (Shukor et al., 2025)</td>
-   <td>2.2B</td><td>93.0</td><td> 94.0 </td><td>91.0</td><td> 77.0 </td><td>88.8</td>
-  </tr>
+  <tr><td>NORA (Hung et al., 2025)</td>
+   <td>3B</td><td>92.2 </td><td>95.4 </td><td>89.4</td><td> 74.6 </td><td>87.9</td></tr>
 
-  <tr>
-   <td>GR00T N1 (NVIDIA et al., 2025)</td>
-   <td>2B</td><td>94.4</td><td> 97.6 </td><td>93.0 </td><td>90.6</td><td> 93.9</td>
-  </tr>
+  <tr><td>SmolVLA (Shukor et al., 2025)</td>
+   <td>2.2B</td><td>93.0</td><td> 94.0 </td><td>91.0</td><td> 77.0 </td><td>88.8</td></tr>
 
-  <tr>
-   <td>GraspVLA (Deng et al., 2025)</td>
-   <td>1.8B</td><td>-</td><td> 94.1 </td><td>91.2 </td><td>82.0</td><td> 89.1</td>
-  </tr>
+  <tr><td>GR00T N1 (NVIDIA et al., 2025)</td>
+   <td>2B</td><td>94.4</td><td> 97.6 </td><td>93.0 </td><td>90.6</td><td> 93.9</td></tr>
 
-  <tr>
-    <td rowspan="4">Tiny-scale</td>
-   <td>Seer (Tian et al., 2025)</td>
-   <td>0.57B</td><td>-</td><td> - </td><td>- </td><td>78.7</td><td> 78.7</td>
-  </tr>
+  <tr><td rowspan="5">Tiny-scale</td><td>Seer (Tian et al., 2025)</td>
+   <td>0.57B</td><td>-</td><td> - </td><td>- </td><td>78.7</td><td> 78.7</td></tr>
 
-  <tr>
-   <td>VLA-OS (Gao et al., 2025)</td>
-   <td>0.5B</td><td>87.0 </td><td>96.5</td><td> 92.7 </td><td>66.0</td><td> 85.6</td>
-  </tr>
+  <tr><td>VLA-OS (Gao et al., 2025)</td>
+   <td>0.5B</td><td>87.0 </td><td>96.5</td><td> 92.7 </td><td>66.0</td><td> 85.6</td></tr>
 
-  <tr>
-   <td>Diffusion Policy (Chi et al., 2023)</td>
-   <td>-</td><td>78.3</td><td> 92.5</td><td> 68.3 </td><td>50.5 </td><td>72.4</td>
-  </tr>
+  <tr><td>Diffusion Policy (Chi et al., 2023)</td>
+   <td>-</td><td>78.3</td><td> 92.5</td><td> 68.3 </td><td>50.5 </td><td>72.4</td></tr>
 
-  <tr>
-   <td><b>VLA-Adapter (Ours)</b></td>
-   <td><b>0.5B</b></td><td><b>97.8</b></td><td> <b>99.2</b> </td><td><i><u>97.2*</u></i></td><td> <b>95.0</b></td><td><b>97.3</b></td>
-  </tr>
+  <tr><td><b>VLA-Adapter (Ours)</b></td>
+   <td><b>0.5B</b></td><td><b>97.8</b></td><td><b>99.2</b></td><td><i><u>97.2*</u></i></td><td> <b>95.0 </b></td><td><b>97.3</b></td></tr>
 
-  <tr>
-   <td><b>VLA-Adapter (Pro)</b></td>
-   <td><b>0.5B</b></td><td><b>97.8</b></td><td> <b>99.2</b> </td><td><i><u>97.2*</u></i></td><td> <b>96.4</b></td><td><b>97.3</b></td>
-  </tr>
+  <tr><td><b>VLA-Adapter-Pro (Ours)</b></td>
+   <td><b>0.5B</b></td><td><b><i>99.6</i></b></td><td><b><i>99.6</i></b> </td><td><b><i>98.2</i></b></td><td><b><i>96.4</i></b></td><td><b><i>98.5</i></b></td></tr>
   
 </table>
 
-### Effectiveness Comparison
 
 <table>
   <tr>
-  <td></td>
-   <td><strong>OpenVLA-OFT</strong></td>
-   <td><strong>VLA-Adapter</strong></td>
-  <td></td>
+   <td><strong>CALVIN</strong></td>  <td><strong>Methods</strong></td>
+   <td><strong>Scale</strong></td>  <td><strong>1</strong></td>
+   <td><strong>2</strong></td>  <td><strong>3</strong></td>
+   <td><strong>4</strong></td>  <td><strong>5</strong></td> <td><strong>Avg. len</strong></td>
   </tr>
 
-  <tr>
-  <td>Backbone</td>
-   <td>7B</td>
-   <td><strong>0.5B</strong></td>
-    <td>1/14×</td>
-  </tr>
+  <tr><td rowspan="8">Large-scale</td><td>UniVLA (Bu et al., 2025) </td><td>7B </td><td>95.5 </td><td>85.8 </td><td>75.4</td><td> 66.9 </td><td>56.5 </td><td>3.80</tr>
 
-  <tr>
-  <td>Fine-Tuning Cost</td>
-   <td>304GPU·h</td>
-   <td><strong>8GPU·h</strong></td>
-    <td>1/38×</td>
-  </tr>
-  
-  <tr>
-  <td>Training VRAM (8 batch)</td>
-   <td>62GB</td>
-   <td><strong>24.7GB</strong></td>
-    <td>0.4×</td>
-  </tr>
+  <tr><td>OpenVLA (Kim et al., 2024) </td><td> 7B</td><td> 91.3</td><td> 77.8 </td><td>62.0 </td><td>52.1 </td><td>43.5</td><td> 3.27</td></tr>
 
-  <tr>
-  <td>Throughput (8 chunk)</td>
-   <td>71.4Hz</td>
-   <td><strong>219.2Hz</strong></td>
-    <td>3×</td>
-  </tr>
+  <tr><td>OpenVLA-OFT (Kim et al., 2025)</td><td> 7B</td><td> 96.3</td><td> 89.1 </td><td>82.4</td><td> 75.8</td><td> 66.5</td><td> 4.10</td></tr>
 
-  <tr>
-  <td>Performance</td>
-   <td>97.1%</td>
-   <td><strong>97.3%</strong></td>
-    <td>Maintain</td>
-  </tr>
+  <tr><td>VLAS (Zhao et al., 2025b) </td><td> 7B</td><td> 87.2 </td><td>64.2</td><td> 40.9 </td><td>28.1</td><td> 19.6 </td><td>2.40</td></tr>
+
+  <tr><td>LCB (Shentu et al., 2024) </td><td> 7B</td><td> 73.6 </td><td>50.2 </td><td>28.5 </td><td>16.0 </td><td>9.9 </td><td>1.78</td></tr>
+
+  <tr><td>RoboDual (Bu et al., 2024a) </td><td> 7B</td><td> 94.4</td><td> 82.7</td><td> 72.1</td><td> 62.4 </td><td>54.4</td><td> 3.66</td></tr>
+
+  <tr><td>OpenHelix (Cui et al., 2025)  </td><td> 7B</td><td> <i><u>97.1*</u></i> </td><td>91.4 </td><td>82.8</td><td> 72.6</td><td> 64.1 </td><td>4.08</td></tr>
+
+  <tr><td>ReconVLA (Song et al., 2025c)  </td><td> 7B</td><td> 95.6 </td><td>87.6 </td><td>76.9</td><td> 69.3</td><td> 64.1 </td><td>3.95</td></tr>
+
+  <tr><td rowspan="4">Small-scale</td><td>DeeR (Yue et al., 2024) </td><td> 3B</td><td> 86.2</td><td> 70.1 </td><td>51.8</td><td> 41.5</td><td> 30.4 </td><td>2.82</td></tr>
+
+  <tr><td>RoboFlamingo (Li et al., 2024b) </td><td> 3B</td><td> 82.4 </td><td>61.9</td><td> 46.6 </td><td>33.1</td><td> 23.5</td><td> 2.48</td></tr>
+
+  <tr><td>VPP (Hu et al., 2025)</td><td>  1.5B</td><td>  95.7</td><td>  91.2</td><td>  <i><u>86.3*</u></i></td><td>  <i><u>81.0*</u></i></td><td>  <i><u>75.0*</u></i></td><td>  <i><u>4.33*</u></i></td></tr>
+
+  <tr><td>SuSIE (Black et al., 2024)</td><td>1.3B</td><td> 87.0</td><td> 69.0</td><td> 49.0 </td><td>38.0</td><td> 26.0</td><td> 2.69</td></tr>
+
+  <tr><td rowspan="5">Tiny-scale</td><td>Seer-Large (Tian et al., 2025)</td><td>0.57B</td><td> 96.3 </td><td><i><u>91.6*</u></i></td><td> 86.1 </td><td>80.3 </td><td>74.0</td><td> 4.28</td></tr>
+
+  <tr><td>MoDE (Reuss et al., 2025) </td><td> 0.44B </td><td>96.2</td><td> 88.9</td><td> 81.1</td><td> 71.8 </td><td>63.5 </td><td>4.01</td></tr>
+
+  <tr><td>Seer (Tian et al., 2025) </td><td> 0.32B</td><td> 94.4 </td><td>87.2 </td><td>79.9 </td><td>72.2 </td><td>64.3</td><td> 3.98</td></tr>
+
+  <tr><td><b>VLA-Adapter (Ours)</b></td>
+   <td><b>0.5B</b></td><td><b><i>99.1</i></b> </td><td><b>94.6</b> </td><td><b>88.8</b></td><td> <b>82.8</b> </td><td><b>76.5</b> </td><td><b>4.42</b></td></tr>
+
+  <tr><td><b>VLA-Adapter-Pro (Ours)</b></td>
+   <td><b>0.5B</b></td><td><b>98.5</b></td><td><b><i>95.0</i></b> </td><td><b><i>90.5</i></b></td><td><b><i>85.3</i></b></td><td><b><i>80.0</i></b></td><td><b><i>4.50</i></b></td></tr>
+  
 </table>
 
 ## Citation instructions
 
 ```BibTeX
-@article{Wang2025VLAAdapter,
-  author = {Wang, Yihao and Ding, Pengxiang and Li, Lingxiao and Cui, Can and Ge, Zirui and Tong, Xinyang and Song, Wenxuan and Zhao, Han and Zhao, Wei and Hou, Pengxu and Huang, Siteng and Tang, Yifan and Wang, Wenhui and Zhang, Ru and Liu, Jianyi and Wang, Donglin},
-  title = {VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model},
-  journal = {ArXiv},
-  year = {2025}
+@article{wang2025vlaadapter,
+  author={Wang, Yihao and Ding, Pengxiang and Li, Lingxiao and Cui, Can and Ge, Zirui and Tong, Xinyang and Song, Wenxuan and Zhao, Han and Zhao, Wei and Hou, Pengxu and Huang, Siteng and Tang, Yifan and Wang, Wenhui and Zhang, Ru and Liu, Jianyi and Wang, Donglin},
+  title={VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model},
+  journal={arXiv preprint arXiv:2509.09372},
+  year={2025}
 }
 ```