GameQA-5K: 5,000 Synthetic Multimodal Reasoning Samples for Vision-Language Models

GameQA-5K is a dataset of 5,000 training samples extracted from the larger GameQA-140K dataset. It was created by the OpenMOSS-Team and published on Hugging Face in June 2025 for use in training models via the GRPO method. The data is synthesized from game code to enhance multimodal reasoning in vision-language models.

Use Cases

• Training vision-language models for visual question answering based on the multimodal reasoning data.
• Fine-tuning models for general reasoning tasks using the synthetic data generated from game code.
• Benchmarking model performance on multimodal tasks as suggested by the dataset's platform tags.
• Studying the effects of code-driven data synthesis on model capabilities as described in the associated paper.

Strengths

• Contains 5,000 specifically curated training samples.
• Derived from a larger 140,000-sample dataset, suggesting a consistent underlying data generation process.
• Dataset was updated on 2025-06-22, indicating recent maintenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is known only for this subset; the full dataset's scale is not specified here.
• The description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

OpenMOSS-Team via Hugging Face.

Collection Method

Synthesized from game code, as described in the associated paper 'Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning'.

Time Range

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Freshness

Last updated 2025-06-22 05:16:03.

Geography

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