{"ID":2849771,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23691","arxiv_id":"2510.23691","title":"Game-TARS: Pretrained Foundation Models for Scalable Generalist Multimodal Game Agents","abstract":"We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approaches, this paradigm enables large-scale continual pre-training across heterogeneous domains, including OS, web, and simulation games. Game-TARS is pre-trained on over 500B tokens with diverse trajectories and multimodal data. Key techniques include a decaying continual loss to reduce causal confusion and an efficient Sparse-Thinking strategy that balances reasoning depth and inference cost. Experiments show that Game-TARS achieves about 2 times the success rate over the previous sota model on open-world Minecraft tasks, is close to the generality of fresh humans in unseen web 3d games, and outperforms GPT-5, Gemini-2.5-Pro, and Claude-4-Sonnet in FPS benchmarks. Scaling results on training-time and test-time confirm that the unified action space sustains improvements when scaled to cross-game and multimodal data. Our results demonstrate that simple, scalable action representations combined with large-scale pre-training provide a promising path toward generalist agents with broad computer-use abilities.","short_abstract":"We present Game-TARS, a generalist game agent trained with a unified, scalable action space anchored to human-aligned native keyboard-mouse inputs. Unlike API- or GUI-based approaches, this paradigm enables large-scale continual pre-training across heterogeneous domains, including OS, web, and simulation games. Game-TA...","url_abs":"https://arxiv.org/abs/2510.23691","url_pdf":"https://arxiv.org/pdf/2510.23691v1","authors":"[\"Zihao Wang\",\"Xujing Li\",\"Yining Ye\",\"Junjie Fang\",\"Haoming Wang\",\"Longxiang Liu\",\"Shihao Liang\",\"Junting Lu\",\"Zhiyong Wu\",\"Jiazhan Feng\",\"Wanjun Zhong\",\"Zili Li\",\"Yu Wang\",\"Yu Miao\",\"Bo Zhou\",\"Yuanfan Li\",\"Hao Wang\",\"Zhongkai Zhao\",\"Faming Wu\",\"Zhengxuan Jiang\",\"Weihao Tan\",\"Heyuan Yao\",\"Shi Yan\",\"Xiangyang Li\",\"Yitao Liang\",\"Yujia Qin\",\"Guang Shi\"]","published":"2025-10-27T17:43:51Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}