Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+datasets:
+- Code2Logic/GameQA-140K
+- Code2Logic/GameQA-5K
+base_model:
+- OpenGVLab/InternVL2_5-8B
+---
+
+***This model (GameQA-InternVL2.5-8B) results from training InternVL2.5-8B with GRPO solely on our [GameQA-5K](https://huggingface.co/datasets/Code2Logic/GameQA-5K) (sampled from the full [GameQA-140K](https://huggingface.co/datasets/Gabriel166/GameQA-140K) dataset).***
+
+# Evaluation Results on General Vision BenchMarks
+
+<div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/evaluation_results_on_general_vision_benchmarks.png"></div>
+
+***(The inference and evaluation configurations were unified across both the original open-source models and our trained models.)***
+
+# 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 with a GRPO strategy solely on **GameQA** (synthesized via our proposed **Code2Logic** approach), multiple cutting-edge open-source models exhibit significantly enhanced out-of-domain generalization.
+
+[[📖 Paper](https://arxiv.org/abs/2505.13886)] [[🤗 GameQA-140K Dataset](https://huggingface.co/datasets/Gabriel166/GameQA-140K)] [[🤗 GameQA-5K Dataset](https://huggingface.co/datasets/Code2Logic/GameQA-5K)] [[🤗 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) ]
+
+<div align=center><img src="https://raw.githubusercontent.com/tongjingqi/Code2Logic/refs/heads/main/assets/categorized_30_games_images.png"></div>
+
+## News
+
+* We've open-sourced the models trained with GRPO on GameQA on [Huggingface](https://huggingface.co/Code2Logic).