GigaWorld-1: A Roadmap to Build World Models for Robot Policy Evaluation
Authors: name: GigaWorld Team, name: Angyuan Ma, name: Boyuan Wang, name: Bohan Li, name: Chaojun Ni, name: Guo Li, name: Guan Huang, name: Guosheng Zhao, name: Hao Li, name: Hengtao Li, name: Jingyu Liu, name: Jiwen Lu, name: Qiuping Deng, name: Tingdong Yu, name: Xuancheng Xu, name: Xinyu Zhou, name: Xiuwei Xu, name: Xinze Chen, name: Xiaofeng Wang, name: Xiaoyu Tian, name: Yang Wang, name: Yifan Chang, name: Yukun Zhou, name: Yun Ye, name: Zhenyu Wu, name: Zhanqian Wu, name: Zheng Zhu
Organization: GigaAI

Evaluating embodied robot foundation models remains a critical bottleneck; unlike large language models efficiently assessed via digital benchmarks, robotic policies require slow, costly real-world rollouts limited by hardware and human supervision, which has driven interest in world models as surrogate policy evaluators, yet the key properties that make a world model reliable for policy assessment remain poorly understood. This work presents a systematic study of world models for robotic policy evaluation and introduces WMBench, a benchmark constructed from real-robot teleoperation data and matched policy rollouts covering diverse manipulation tasks to enable controlled comparisons across model families, action encodings, rollout horizons, and evaluation metrics. Using WMBench, we analyze 7 video world models, 4 action representation schemes, and over 324,000 simulated policy rollouts paired with real robot executions, further enriching our analysis with large-scale community submissions from the CVPR 2026 GigaBrain Challenge, curated synthetic trajectories, and a training videos spanning more than 12,000 hours. Our experiments deliver three core insights: evaluator quality is dominated by long-horizon, action-faithful rollout consistency rather than short-term visual realism; pretraining gains stem not only from data scale but from balancing general world knowledge with robot-specific controllability; and architectural choices including action encoding, memory design, and evaluator-focused post-training strongly determine alignment with real-world robot behavior. Drawing on these results, we derive a practical design roadmap and realize it in GigaWorld-1, a world model specially optimized for policy evaluation, and we fully release our code, models, datasets, and toolkits to advance scalable evaluation research for embodied foundation models.