Large vision-language models (VLMs) face challenges in achieving robust, transferable reasoning abilities due to reliance on labor-intensive manual instruction datasets or computationally expensive self-supervised methods. To address these issues, we introduce MindGYM, a framework that enhances VLMs through synthetic self-challenging questions, consisting of three stages: (1) Seed Single-Hop Question Synthesis, generating cognitive questions across textual (e.g., logical deduction) and multimodal contexts (e.g., diagram-based queries) spanning eight semantic areas like ethical analysis; (2) Challenging Multi-Hop Question Synthesis, combining seed questions via diverse principles like bridging, visual-textual alignment, to create multi-step problems demanding deeper reasoning; and (3) Thinking-Induced Curriculum Fine-Tuning, a structured pipeline that progressively trains the model from scaffolded reasoning to standalone inference. By leveraging the model's self-synthesis capability, MindGYM achieves high data efficiency (e.g., +16% gains on MathVision-Mini with only 400 samples), computational efficiency (reducing both training and inference costs), and robust generalization across tasks. Extensive evaluations on seven benchmarks demonstrate superior performance over strong baselines, with notable improvements (+15.77% win rates) in reasoning depth and breadth validated via GPT-based scoring. MindGYM underscores the viability of self-challenging for refining VLM capabilities while minimizing human intervention and resource demands. Code and data are released to advance multimodal reasoning research.

本文针对大型视觉-语言模型在获得强大可转移推理能力方面面临的挑战，提出了MindGYM框架。该框架通过合成自我挑战问题，在三个阶段中提高模型的推理能力，并在七个基准上表现出卓越的性能，显著提升了推理的深度和广度。此研究展示了自我挑战在精炼视觉-语言模型能力方面的可行性，同时减少了人力和资源的需求。

MindGYM：通过合成自我挑战问题提升视觉-语言模型