震撼，英伟达新模型能打遍几乎所有游戏

Core Viewpoint - The article introduces Nvidia's latest open-source model, NitroGen, which is capable of playing over 1,000 different games using AI-generated controls, showcasing significant advancements in gaming automation and cross-game adaptability [5][6][8]. Group 1: Model Overview - NitroGen is designed to play a wide variety of game genres, including RPGs, platformers, and racing games, by directly processing game video frames to generate controller signals [6][8]. - The model supports fine-tuning for new games, allowing it to adapt quickly without starting from scratch, demonstrating its potential for cross-game generalization [8]. - The architecture of NitroGen is based on the GR00T N1.5 framework, which was originally designed for robotics but has been adapted for gaming applications with minimal modifications [12]. Group 2: Key Components - NitroGen consists of three core components: a multi-game intelligent agent, a universal simulator, and a large-scale dataset of gaming videos [15][16][17]. - The multi-game intelligent agent can generate controller commands from game observations, enabling zero-shot gameplay across various titles [15]. - The universal simulator standardizes interactions across different games using the Gymnasium API, facilitating large-scale training and evaluation [16]. - The dataset comprises 40,000 hours of publicly available gaming videos, covering over 1,000 games, and includes automatically generated action labels [17][24]. Group 3: Data Collection and Processing - The data collection process involved extracting player actions from videos with "input overlays," which present real-time controller inputs [18][19]. - The research team utilized advanced techniques to match key points and segment the controller displays from the videos, ensuring the model learns without "cheating" [21]. - The dataset features a diverse distribution of game types, with action RPGs making up 34.9% of the total video duration, followed by platformers at 18.4% [26]. Group 4: Performance and Results - NitroGen has demonstrated strong performance across various game types, including 3D action games and 2D platformers, achieving non-trivial task completion rates [28][30]. - The model showed a significant improvement in task success rates when fine-tuned for new games, with up to a 52% relative increase compared to models trained from scratch [32]. - The research indicates that NitroGen is a foundational step towards creating general-purpose embodied agents capable of interacting with complex environments [35][36].