From the source
PROWL-1 is a novel RL-driven adversarial framework where an RL agent explores game environments with the objective to improve world model performance Jeff Hawke May 12th, 2026 Odyssey is pioneering foundation world models.
To power reliable applications in robotics, science, healthcare, education, and gaming, world models must learn to predict high-quality visuals with realistic physics while accurately responding to actions.
Today’s best world models are still not pixel or physics perfect, and they do not always follow the specified action.
To address this, we’ve developed PROWL-1 (Prioritized Regret-Driven Optimization for World Model Learning), a novel RL-driven adversarial framework where an RL agent explores game environments to discover failures in world models.
As the agent interacts with the environment, it is rewarded for uncovering breakdowns in geometry, motion, visual consistency, and action-conditioned dynamics, automatically building an adversarial curriculum that improves the model over time.
PROWL-1 also surfaces cases where the world model captures geometry and physics correctly, but fails to follow actions precisely.
…
