NitroGen：面向通用游戏智能体的开放基础模型

摘要

我们推出NitroGen——一个面向通用游戏智能体的视觉-动作基础模型，该模型基于超过1,000款游戏、总计4万小时的游戏录像训练而成。我们融合了三大核心要素：1）通过自动提取公开游戏视频中的玩家动作构建的网络级视频-动作数据集；2）能够衡量跨游戏泛化能力的多游戏基准环境；3）采用大规模行为克隆训练的统一视觉-动作模型。NitroGen在多个领域展现出强大能力，包括3D动作游戏的战斗场景、2D平台游戏的高精度操控，以及程序生成世界的探索任务。该模型能有效迁移至未训练过的游戏，在任务成功率上相比从头训练的模型最高实现52%的相对提升。我们将公开数据集、评估套件和模型权重，以推动通用具身智能体的研究发展。

English

We introduce NitroGen, a vision-action foundation model for generalist gaming agents that is trained on 40,000 hours of gameplay videos across more than 1,000 games. We incorporate three key ingredients: 1) an internet-scale video-action dataset constructed by automatically extracting player actions from publicly available gameplay videos, 2) a multi-game benchmark environment that can measure cross-game generalization, and 3) a unified vision-action model trained with large-scale behavior cloning. NitroGen exhibits strong competence across diverse domains, including combat encounters in 3D action games, high-precision control in 2D platformers, and exploration in procedurally generated worlds. It transfers effectively to unseen games, achieving up to 52% relative improvement in task success rates over models trained from scratch. We release the dataset, evaluation suite, and model weights to advance research on generalist embodied agents.

NitroGen：面向通用游戏智能体的开放基础模型

NitroGen: An Open Foundation Model for Generalist Gaming Agents

摘要

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