{"url":"/dataset/gameqa","name":"GameQA","full_name":"GameQA-140K","description_markdown":"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.\r\n\r\nPaper: Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning\r\n\r\nCode: https://github.com/tongjingqi/Code2Logic","description_withheld":null,"homepage":"https://huggingface.co/datasets/Code2Logic/GameQA-140K","introduced_date":"2025-05-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/code2logic-game-code-driven-data-synthesis","title":"Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning","first_author":"Jingqi Tong","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Multimodal Reasoning","url":"/task/multimodal-reasoning","datasets_with_task":"/datasets/task/multimodal-reasoning"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["GameQA"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}