Low-light image enhancement (LLIE) aims to restore the visual quality of images captured under poor illumination conditions, a task that remains challenging due to complex degradations such as overexposure, noise, and low contrast. In this paper, we propose a novel curriculum learning framework that facilitates effective LLIE model training by modulating sample selection according to estimated difficulty. Our key insight is that residual signals obtained via intrinsic decomposition capture image characteristics such as color spill, indirect lighting, and saturation, which correlate strongly with reconstruction difficulty. We use the magnitude of these residuals as a proxy for difficulty, enabling a curriculum strategy that begins with easier samples and gradually incorporates more difficult ones. Extensive experiments demonstrate that the proposed method consistently improves performance across various LLIE baseline models and datasets. Being model-agnostic and plug-and-play, our method offers meaningful gains through curriculum learning without requiring additional annotations or architectural modifications.