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Odyssey Systems released Odyssey-3, a world model trained on visual observations that can control robots, drive cars, pilot drones, and train other AIs with minimal task-specific data. The same base model adapts across these diverse physical and virtual systems by learning general physics and cause-and-effect relationships rather than being specialized for each task.
- Odyssey-3 learns robot arm control with tens of hours of demonstrations and shows recovery behaviors not in training data, suggesting it grasps underlying physics rather than memorizing examples.
- With only 20 hours of simulated driving data, it autonomously drove cars in India, performing 77% as well as policies trained on real footage.
- The model can generate simulated environments where AI agents learn and discover world model failures, creating a feedback loop where each intelligence improves the other.
This paper introduces Qwen-AgentWorld, large language models trained to simulate agentic environments across seven domains using over 10 million interaction trajectories. The authors detail a three-stage training pipeline (CPT, SFT, RL) and evaluate on AgentWorldBench, showing superior simulation fidelity. They also demonstrate its use as both a standalone simulator for RL and a warm-up step that boosts downstream agent performance.
- Qwen-AgentWorld's 397B model held correct object-grasp sequences over 25 reasoning steps in a robotics task without losing context, beating existing simulators on state accuracy and reasoning-chain length.
- RL agents trained inside Qwen-AgentWorld's simulated environments outperformed agents trained only in the real world by 15–20%.
- Using the world model's weights to warm-start a unified agent boosted performance across seven benchmarks and sometimes cut required training runs in half.