Game-TARS: Pretrained Foundation Models for Scalable Generalist
  Multimodal Game Agents
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.
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Zihao Wang
claimed_verified
2025-10-30T14:41:55.083Z
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Zihao Wang
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Xujing Li
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Yining Ye
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Junjie Fang
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Haoming Wang
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2025-10-29T12:43:14.628Z
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Haoming Wang
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Longxiang Liu
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Shihao Liang
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Junting Lu
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Zhiyong Wu
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Jiazhan Feng
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2025-10-30T14:41:52.633Z
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Jamie Jiazhan Feng
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jzfeng
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Wanjun Zhong
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Zili Li
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Yu Wang
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Yu Miao
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Bo Zhou
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Yuanfan Li
69018071646208eac0d1f455
Hao Wang
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Zhongkai Zhao
claimed_verified
2025-10-29T12:43:25.797Z
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Zhongkai Zhao
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Faming Wu
69018071646208eac0d1f458
Zhengxuan Jiang
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Weihao Tan
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Heyuan Yao
69018071646208eac0d1f45b
Shi Yan
69018071646208eac0d1f45c
Xiangyang Li
69018071646208eac0d1f45d
Yitao Liang
69018071646208eac0d1f45e
Yujia Qin
69018071646208eac0d1f45f
Guang Shi
Game-TARS: Pretrained Foundation Models for Scalable Generalist
  Multimodal Game Agents
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.
Game-TARS, a generalist game agent trained with a unified action space, achieves superior performance across various domains and benchmarks through large-scale pre-training and efficient reasoning strategies.
unified action space human-aligned native keyboard-mouse inputs decaying continual loss Sparse-Thinking strategy causal confusion open-world Minecraft tasks unseen web 3d games FPS benchmarks cross-game multimodal data generalist agents computer-use abilities