Update README.md

Author: zwbx

README.md

@@ -97,35 +97,34 @@ You can customize these parameters by editing `src/cfgs/launch.yaml` directly, o
   - `.hydra/`: Configuration snapshots and Hydra management files
 - `README.md`：Project documentation
 
-
-## Note on Open-Source Implementation
-
-The open-source implementation slightly differs from the original version reported in the paper. We reconstructed the entire training and evaluation pipeline for better clarity and reproducibility.
-
-During this process, a few settings were adjusted:
-
-- Both the **latent loss** and **action loss** were changed from **L2** to **L1**.  
-- The **multi-token prediction head** was reduced from **5 tokens** to **2 tokens**.
-
-These updates generally lead to improved success rates across most tasks.  
-As a result, your observed performance (e.g., **100% on “push button”**) may exceed the numbers reported in the paper.
-
 ## Experiments Results
 
 ### Evluation over 60 RLBench tasks
 Why we use 60 tasks for the main evaluation?
-Although the 18 RLBench tasks have been widely adopted as a benchmark since their introduction in Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation, they are primarily used to evaluate 3D-based hierarchical policies that depend heavily on high-precision 3D inputs and motion planners. Many of these tasks are extremely challenging for RGB-only visuomotor policies, often leading to uniformly low success rates and therefore limited discriminative power.`
+Although the 18 RLBench tasks have been widely adopted as a benchmark since their introduction in Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation, they are primarily used to evaluate 3D-based hierarchical policies that depend heavily on high-precision 3D inputs and motion planners. Many of these tasks are extremely challenging for RGB-only visuomotor policies, often leading to uniformly low success rates and therefore limited discriminative power.
 
 <img width="1105" height="473" alt="coa_performance" src="https://github.com/user-attachments/assets/b4408c9d-311b-4c42-9cdb-74decfdb91ef" />
 
 
 ### Evluation over 18 RLBench tasks
 
-To enable convenient comparison with 3D-based hierarchical methods—such as RVT-2, we also report results on the RLBench-18 benchmark. Plase check appendix for more details.
+To enable convenient comparison with 3D-based hierarchical methods—such as RVT-2, we also report results on the RLBench-18 benchmark. Plase check appendix for more details. As you can see, there is still substantial room for RGB-only visuomotor policies to close the performance gap.
 
 <img width="706" height="431" alt="coa_rlbench18" src="https://github.com/user-attachments/assets/3b698819-fd0a-4e6e-979e-f64ec108df52" />
 
-## Updated Results (Open-Source Version)
+## Note on Open-Source Implementation
+
+The open-source implementation slightly differs from the original version reported in the paper. We reconstructed the entire training and evaluation pipeline for better clarity and reproducibility.
+
+During this process, a few settings were adjusted:
+
+- Both the **latent loss** and **action loss** were changed from **L2** to **L1**.  
+- The **multi-token prediction head** was reduced from **5 tokens** to **2 tokens**.
+
+These updates generally lead to improved success rates across most tasks.  
+As a result, your observed performance (e.g., **100% on “push button”**) may exceed the numbers reported in the paper.
+
+### Updated Results (Open-Source Version)
 
 For reference, below are the task-level success rates of the open-source implementation compared with those reported in the paper.  
 The open-source version generally achieves higher performance due to the modified training configuration.