{"ID":2822914,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.02427","arxiv_id":"2601.02427","title":"NitroGen: An Open Foundation Model for Generalist Gaming Agents","abstract":"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.","short_abstract":"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 avail...","url_abs":"https://arxiv.org/abs/2601.02427","url_pdf":"https://arxiv.org/pdf/2601.02427v1","authors":"[\"Loïc Magne\",\"Anas Awadalla\",\"Guanzhi Wang\",\"Yinzhen Xu\",\"Joshua Belofsky\",\"Fengyuan Hu\",\"Joohwan Kim\",\"Ludwig Schmidt\",\"Georgia Gkioxari\",\"Jan Kautz\",\"Yisong Yue\",\"Yejin Choi\",\"Yuke Zhu\",\"Linxi \\\"Jim\\\" Fan\"]","published":"2026-01-04T16:24:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.LG\"]","methods":"[\"LoRA\"]","has_code":false}