This article introduces Reinforcement World Model Learning (RWML), a method that helps large language models (LLMs) better predict the outcomes of their actions in various environments. By using self-supervised learning to align simulated and actual states, RWML improves the agents' ability to adapt and succeed in tasks without requiring external rewards. The authors demonstrate significant performance gains on benchmark tasks compared to traditional approaches.