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
Organization: ByteDance

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.