Core Viewpoint - The research introduces a groundbreaking method called ViGaL (Visual Game Learning), which enhances multi-modal reasoning capabilities in AI models through game training, without the need for extensive mathematical training samples [5][11][24]. Group 1: Research Findings - The study demonstrates that training AI models on simple games like Snake can significantly improve their performance in mathematical reasoning and multi-disciplinary tasks, achieving an average accuracy increase of 2.9% on mathematical benchmarks and 2.0% on multi-disciplinary reasoning tasks [11][15]. - The research team utilized a 7B parameter model, Qwen2.5-VL, and found that reinforcement learning through game play outperformed traditional methods that relied on mathematical or multi-disciplinary data [11][15]. - The findings suggest that game training can lead to stronger cross-domain generalization, allowing models to transfer skills learned in gaming to complex reasoning tasks in mathematics and other fields [7][11]. Group 2: Game Design and Training Methodology - The research involved two complementary training games: Snake, which focuses on path planning and spatial navigation, and a custom-designed 3D rotation game that enhances spatial geometric understanding [18][19]. - The design philosophy of the games is complementary, with Snake improving 2D coordinate-related mathematical performance and the rotation game targeting angle and length reasoning [20]. - Joint training on both games proved to be more effective than training on either game alone, showcasing the potential for diverse gaming tasks to enhance AI performance [20]. Group 3: Implications and Future Directions - The success of ViGaL indicates a potential new trend in AI training, suggesting that well-designed games could serve as synthetic tasks to develop multi-modal reasoning capabilities when high-quality human data is scarce [22][23]. - This game-based training paradigm offers unique advantages over traditional methods, emphasizing the importance of cultivating underlying general reasoning abilities rather than solely focusing on direct task learning [23]. - The research highlights that allowing AI to "play games" may be more effective than conventional training methods, especially as challenges arise in scaling traditional approaches [24].

故事的开始总是充满挑战。当我们将任务交给AI时，它就像一个刚刚出生的婴儿，对这个世界一无所 知。 我们要求AI用Python编写一个简单的"贪吃蛇"游戏，并让它自己玩游戏。 听起来是不是很简单？ 但其实，这只是一个开始。 AI的第一步是创建游戏。它需要理解游戏的规则：蛇如何移动，如何吃水果，如何避免撞到自己。这个 过程并不容易，但AI凭借其强大的计算能力，很快就给出了答案。它不仅成功创建了游戏，还编写了一 个脚本，让蛇能够自动移动并吃水果。 我们看着蛇在屏幕上自动移动，吃掉一个个水果，心中充满了惊喜。这只是一个简单的游戏，但对于AI 来说，这是一个巨大的进步。它不仅理解了游戏规则，还能够通过代码实现这些规则。这就像一个孩子 第一次学会走路，虽然还很笨拙，但已经迈出了重要的一步。 遇到挑战：AI的第一次失败 然而，事情并没有那么简单。我们决定增加一些难度，让游戏变得更加复杂。 我们在游戏中加入了陷 阱，每两秒会出现一个障碍物，蛇一旦撞到就会失去一部分身体。我们想看看AI是否能够应对这种新的 挑战。 结果并不理想。AI编写的脚本在面对陷阱时显得有些无能为力。蛇不断地撞到陷阱，失去了很多分数。 我们意识到，AI虽然 ...