The potential limitations of using linear cumulants in GOMs for modeling future outcomes lie in the assumption that rewards are perfectly represented as linear combinations of features. In reality, many environments have non-linear relationships between state features and rewards, making it challenging for linear cumulants to accurately capture the dynamics of the environment. This limitation may lead to suboptimal policies when dealing with tasks that exhibit significant non-linearity in their reward functions.