GOMs can be further improved to handle non-linear reward functions effectively by incorporating more expressive feature learning methods for cumulants. Instead of relying on linear relationships between rewards and cumulants, GOMs can leverage advanced techniques such as deep neural networks or kernel methods to capture the non-linear mappings between rewards and features. By using these more sophisticated models, GOMs can better represent complex reward structures and improve their adaptability to a wider range of tasks with non-linear reward functions.