Update README.md

README.md

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 - Qwen/Qwen2.5-VL-7B-Instruct
 ---
 
+***This model (GameQA-Qwen2.5-VL-7B) results from trainning Qwen2.5-VL-7B with GRPO purely on our [GameQA](https://huggingface.co/datasets/Gabriel166/GameQA-140K) dataset.***
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+# Evaluation Results on General Vision BenchMarks
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+<div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/evaluation_results_on_general_vision_benchmarks.png"></div>
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+It's also found that getting trained on 5k samples from our GameQA dataset can lead to better results than on [multimodal-open-r1-8k-verified](https://huggingface.co/datasets/lmms-lab/multimodal-open-r1-8k-verified).
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+<div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/GameQA_generalizes_better.png"></div>
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+# Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning
+
+This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained solely with a GRPO strategy on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
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+[[📖 Paper](https://arxiv.org/abs/2505.13886)] [🤗 [GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [🤗 [GameQA-InternVL3-8B](https://huggingface.co/Code2Logic/GameQA-InternVL3-8B) ] [🤗 [GameQA-Qwen2.5-VL-7B](https://huggingface.co/Code2Logic/GameQA-Qwen2.5-VL-7B)] [🤗 [GameQA-LLaVA-OV-7B](https://huggingface.co/Code2Logic/GameQA-llava-onevision-qwen2-7b-ov-hf) ]
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+<div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/categorized_30_games_images.png"></div>
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 # Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning
 
 This is the first work, to the best of our knowledge, that leverages ***game code*** to synthesize multimodal reasoning data for ***training*** VLMs. Furthermore, when trained solely with a GRPO strategy on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.