CryptoWorld News reports that Odyssey has integrated Reinforcement Learning (RLHF) into visual model training, releasing the Prowl framework, which for the first time introduces RL into the training loop of world models. The framework dispatches RL agents to explore in game environments, seeking failure cases in geometry, motion, visual consistency, and action response, and packages these bugs into training data to feed back into the model. Prowl has designed a Priority Adversarial Trajectory Buffer (PAT), which automatically pushes more difficult failure cases after the model fixes simple bugs. The team validated Prowl in the Minecraft Minerl environment, and quantitative results show that Prowl reduces action following error by 12.6% compared to the pretraining baseline, with the reduction expanding to 20.9% in the top 10% most difficult segments.