Researchers propose a new framework for designing reward functions in reinforcement learning called Module Level Reward Evolution Framework (MLREF). The framework uses large language models to generate and refine reusable components of the reward function. These components are stored in a 'module pool' that evolves over time, allowing effective components to be reused and underperforming ones to be refined. This approach is shown to improve performance by 25.2% in locomotion tasks and 6.6% in manipulation tasks compared to strong baselines. --- Why it matters: This matters because it addresses a bottleneck in reinforcement learning: designing reward functions that are effective and stable across iterations. By reusing and refining components, MLREF can lead to more efficient and robust optimization dynamics, which is crucial for real-world applications of AI. Source: https://arxiv.org/abs/2608.18827