Update README.md

README.md

@@ -23,4 +23,12 @@ stage uses LVLMs to generate rich and accurate captions. Subsequently, the secon
 caption quality by using a vision-only LLM to perform the QA task. We also created a specific QA
 curation pipeline to ensure the quality of the questions and answers used for the second stage.
 
-By employing CapRL training framework, initializing with the Qwen2.5-VL-3B model, and using a carefully filtered 75K QA dataset as the training set, we obtained a highly capable captioner, CapRL-3B.
+By employing CapRL training framework, initializing with the Qwen2.5-VL-3B model, and using a carefully 
+filtered 75K QA dataset as the training set, we obtained a highly capable captioner, CapRL-3B.
+
+## Key Features
+* **Remarkable visual understanding for Chart, Infographics and Document**: CapRL-3B achieves perception accuracy and visual information coverage comparable to Qwen2.5-VL-72B.
+* **Well-organized output**: The outputs of CapRL-3B are relatively well-structured, making them clear and easy to understand.
+* **Detailed description for natural images**: The outputs of CapRL-3B can perfectly cover all valid visual information while containing fewer hallucinations.
+
+## Cases