An emerging area of research aims to learn deep generative models with limited training data. Implicit Maximum Likelihood Estimation (IMLE), a recent technique, successfully addresses the mode collapse issue of GANs and has been adapted to the few-shot setting, achieving state-of-the-art performance. However, current IMLE-based approaches encounter challenges due to inadequate correspondence between the latent codes selected for training and those drawn during inference. This results in suboptimal test-time performance. To address this issue, we propose RS-IMLE, a novel approach that changes the prior distribution used for training. This leads to substantially higher-quality image generation compared to existing IMLE-based methods, as validated by a theoretical analysis and comprehensive experiments conducted on nine few-shot image datasets.