Game-TARS: Pretrained Foundation Models for Scalable Generalist
  Multimodal Game Agents
Authors: name: Zihao Wang; name: Xujing Li; name: Yining Ye; name: Junjie Fang; name: Haoming Wang; name: Longxiang Liu; name: Shihao Liang; name: Junting Lu; name: Zhiyong Wu; name: Jiazhan Feng; name: Wanjun Zhong; name: Zili Li; name: Yu Wang; name: Yu Miao; name: Bo Zhou; name: Yuanfan Li; name: Hao Wang; name: Zhongkai Zhao; name: Faming Wu; name: Zhengxuan Jiang; name: Weihao Tan; name: Heyuan Yao; name: Shi Yan; name: Xiangyang Li; name: Yitao Liang; name: Yujia Qin; name: Guang Shi
Published: 2025-10-27 17:43:51

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.