GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning
Authors: name: GigaBrain Team, name: Boyuan Wang, name: Chaojun Ni, name: Guan Huang, name: Guosheng Zhao, name: Hao Li, name: Jie Li, name: Jindi Lv, name: Jingyu Liu, name: Lv Feng, name: Mingming Yu, name: Peng Li, name: Qiuping Deng, name: Tianze Liu, name: Xinyu Zhou, name: Xinze Chen, name: Xiaofeng Wang, name: Yang Wang, name: Yifan Li, name: Yifei Nie, name: Yilong Li, name: Yukun Zhou, name: Yun Ye, name: Zhichao Liu, name: Zheng Zhu
Organization: GigaAI

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}.