GameQA

GameQA is a large-scale, diverse, and challenging multimodal reasoning dataset designed to enhance the general reasoning capabilities of Vision Language Models (VLMs). Generated using the innovative Code2Logic framework, it leverages game code to synthesize high-quality visual-language Chain-of-Thought (CoT) data. The dataset addresses the scarcity of multimodal reasoning data, critical for advancing complex multi-step reasoning in VLMs. Each sample includes visual game state, targeted question, original analysis, augmented step-by-step reasoning (refinement) and final answer, derived from the logical structures inherent in game code.

Paper: Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning

Code: https://github.com/tongjingqi/Code2Logic