Orca 建模世界状态而非具体动作，性能媲美顶级机器人

baai introduced orca, a 4b parameter model that learns an abstract internal representation of world dynamics from 125,000 hours of video, rather than predicting specific tokens or actions. unlike conventional models, orca uses a frozen core with swappable 'heads' for text, images, and robot control to handle diverse outputs. it matches specialized robotics performance on five tasks despite training on only one-tenth of the video corpus and using zero action labels. this architecture signals a shift toward general world models that alleviate robotics' chronic data scarcity by learning physics and causality from raw video.