Researchers propose a new framework called QWM that combines Q-learning with world models to improve the performance of reinforcement learning agents. This approach leverages world models to predict state changes and select high-value actions during online rollouts and evaluation. The authors claim that QWM avoids compounding model bias while still gaining sample-efficiency benefits, and demonstrate its effectiveness on challenging manipulation benchmarks. --- Why it matters: This work matters because it addresses a key challenge in reinforcement learning: scaling to large, high-dimensional problems such as real-world robotics. By combining Q-learning with world models, the authors provide a more efficient and effective approach to training policies for complex tasks. Source: https://arxiv.org/abs/2608.17163