  This paper explores the use of model-based offline reinforcement learning
with long model rollouts. While some literature criticizes this approach due to
compounding errors, many practitioners have found success in real-world
applications. The paper aims to demonstrate that long rollouts do not
necessarily result in exponentially growing errors and can actually produce
better Q-value estimates than model-free methods. These findings can
potentially enhance reinforcement learning techniques.
