Update README.md

README.md

@@ -13,14 +13,22 @@ pipeline_tag: visual-question-answering
 ## Model Overview
 - **INFRL-Qwen2.5-VL-72B-Preview** improves visual reasoning upon [Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct) model.
 
-- As of March 25th, 2025, **INFRL-Qwen2.5-VL-72B-Preview** is the best-performing open-sourced VL model on various visual reasoning benchmarks ([MathVision](https://mathllm.github.io/mathvision/),[MathVista](https://mathvista.github.io/), [EMMA](https://emma-benchmark.github.io/#leaderboard), [MMMUPro](https://mmmu-benchmark.github.io/), [MathVerse](https://mathverse-cuhk.github.io/)). 
+- As of March 25th, 2025, **INFRL-Qwen2.5-VL-72B-Preview** is the best-performing open-sourced VL model on various visual reasoning benchmarks ([MathVision](https://mathllm.github.io/mathvision/),[MathVista](https://mathvista.github.io/),  [MathVerse](https://mathverse-cuhk.github.io/)). 
 
 
 ## Evaluation
 
-We will release a code repository with vLLM support for VLM evaluation. 
-  - 10x faster than hf.generate(). Better efficiency in evaluating larger benchmark.
-  - Efficient answer extraction and matching with aligned performance with Qwen2.5-VL. No need for costly LLM-Judge.
+| Models            | MathVision (test) | MathVista (testmini) | MathVerse (testmini) |
+|-------------------|-------------------|----------------------|----------------------|
+| GPT4o (R1-1V Rep) | 30.6              | 60                   | 41.2                 |
+| Gemini-2.0-Flash  | 41.3              | 70.1                 | 50.6                 |
+| Claude 3.5 Sonnet | 33.5              | 67.7                 | 47.8                 |
+| QvQ-72B           | 35.9              | 71.4                 | 48.6                 |
+| InternVL2.5-78B   | 34.9              | 72.3                 | 51.7                 |
+| Qwen-VL-2.5-72B   | 38.1              | 74.8                 | 57.18                |
+| INFRL-VL-Preview  | 41.9              | 77.8                 | 58.84                |
+
+We will release a code repository for VLM evaluation. It supports RL training with simple rule-based rewards, meanwhile aligning with LLM-Judge results. 
     
 Stay tuned!