GR-2 is an advanced general-purpose robotic agent specifically designed for diverse and generalizable robotic operations. It undergoes extensive pre-training on a large dataset of internet videos to capture the dynamics of the world. This large-scale pre-training involves 38 million video clips and over 50 billion tags, enabling GR-2 to generalize across a wide range of robotic tasks and environments during subsequent policy learning. Subsequently, GR-2 is fine-tuned for video generation and action prediction using robotic trajectories. It demonstrates impressive multi-task learning capabilities, achieving an average success rate of 97.7% over more than 100 tasks. Moreover, GR-2 excels in new, previously unseen scenarios, including new backgrounds, environments, objects, and tasks. Notably, GR-2 efficiently scales with increasing model size, highlighting its potential for continuous growth and application.