In weakly coupled few mode fibers (FMFs), used in optical communication systems to increase data capacity, the difference between the effective refractive indices of successive spatial modes is a critical parameter. In this paper, we demonstrate that nanostructured FMFs composed of two different types of subwavelength glass rods can be used to effectively control the propagation properties of individual spatial modes. However, the design and optimization of such fibers proves challenging due to the large number of parameters associated with their free-form refractive index distribution. To address this problem, we use an optimization method based on generative inverse design networks (GIDNs) modified to account for the optical properties of weakly coupled FMFs. Our results show that a nanostructured FMF with a core containing multiple angularly modulated concentric rings can achieve differences between the effective refractive indices of 10 spatial modes exceeding 1.2 × 10⁻³, surpassing other forms of weakly coupled FMFs. Importantly, the parameters of the structure ensure the feasibility of fabricating the designed fibers. Compared to traditional FMF optimization using a deep neural network with a large, randomly generated data set, our method achieves comparable accuracy in predicting refractive index differences, while achieving better spatial mode separation with a six times smaller data set. By using our proposed approach, which could potentially be applied to the design of other types of optical fibers characterized by a large number of parameters, we can significantly reduce the number of training examples required and the time needed to achieve high-quality results.