GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning
Vision-language-action (VLA) models that directly predict multi-step action chunks from current observations face inherent limitations due to constrained scene understanding and weak future anticipation capabilities. In contrast, video world models pre-trained on web-scale video corpora exhibit robust spatiotemporal reasoning and accurate future prediction, making them a natural foundation for enhancing VLA learning. Therefore, we propose GigaBrain-0.5M*, a VLA model trained via world model-based reinforcement learning. Built upon GigaBrain-0.5, which is pre-trained on over 10,000 hours of robotic manipulation data, whose intermediate version currently ranks first on the international RoboChallenge benchmark. GigaBrain-0.5M* further integrates world model-based reinforcement learning via RAMP (Reinforcement leArning via world Model-conditioned Policy) to enable robust cross-task adaptation. Empirical results demonstrate that RAMP achieves substantial performance gains over the RECAP baseline, yielding improvements of approximately 30\% on challenging tasks including Laundry Folding, Box Packing, and Espresso Preparation. Critically, GigaBrain-0.5M^* exhibits reliable long-horizon execution, consistently accomplishing complex manipulation tasks without failure as validated by real-world deployment videos on our https://gigabrain05m.github.io{project page}.
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GigaBrain Team
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Boyuan Wang
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Chaojun Ni
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Guan Huang
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Guosheng Zhao
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Hao Li
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Jie Li
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Jindi Lv
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Jingyu Liu
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Lv Feng
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Mingming Yu
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Peng Li
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Qiuping Deng
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Tianze Liu
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Xinyu Zhou
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Xinze Chen
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Xiaofeng Wang
claimed_verified
2026-02-13T09:36:41.892Z
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JeffWang
Jeff-Wang
user
Jeff-Wang
698e8ff2cace060ff123ac6a
Yang Wang
claimed_verified
2026-02-13T09:36:39.496Z
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Yang Wang
supermodelteam
user
supermodelteam
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Yifan Li
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Yifei Nie
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Yilong Li
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Yukun Zhou
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Yun Ye
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Zhichao Liu
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Zheng Zhu
GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning
Vision-language-action (VLA) models that directly predict multi-step action chunks from current observations face inherent limitations due to constrained scene understanding and weak future anticipation capabilities. In contrast, video world models pre-trained on web-scale video corpora exhibit robust spatiotemporal reasoning and accurate future prediction, making them a natural foundation for enhancing VLA learning. Therefore, we propose GigaBrain-0.5M*, a VLA model trained via world model-based reinforcement learning. Built upon GigaBrain-0.5, which is pre-trained on over 10,000 hours of robotic manipulation data, whose intermediate version currently ranks first on the international RoboChallenge benchmark. GigaBrain-0.5M* further integrates world model-based reinforcement learning via RAMP (Reinforcement leArning via world Model-conditioned Policy) to enable robust cross-task adaptation. Empirical results demonstrate that RAMP achieves substantial performance gains over the RECAP baseline, yielding improvements of approximately 30\% on challenging tasks including Laundry Folding, Box Packing, and Espresso Preparation. Critically, GigaBrain-0.5M^* exhibits reliable long-horizon execution, consistently accomplishing complex manipulation tasks without failure as validated by real-world deployment videos on our https://gigabrain05m.github.io{project page}.
A vision-language-action model enhanced with world model-based reinforcement learning demonstrates improved performance and long-horizon execution capabilities for robotic manipulation tasks.
Vision-language-action models world models reinforcement learning cross-task adaptation RAMP RoboChallenge benchmark robotic manipulation