A major challenge in Low-Light Image Enhancement (LLIE) is its ill-posed nature: low-light images often lack sufficient information to align with normal-light ones (e.g., not all training data can be fully fitted to the ground truth). Numerous studies have attempted to bridge the gap between low- and normal-light data by introducing effective additional information, which is called "references" in this paper. However, existing methods overlook the valuable references hidden within the training dataset itself. In this work, we propose a novel LLIE strategy that simultaneously learns image-specific features by neural networks while formulating effective common features from the training data as the reference. These common features are correlated with the samples that are not fully fitted by the LLIE network itself, and they are represented as a set of Learnable Feature Patches and Vectors (LFPVs) in the hidden feature space. LFPVs are updated through two mechanisms: the sample-updater, which extracts useful features from training samples to refine LFPVs, and the mutual-updater, which propagates information across LFPVs to mutually update them. LFPVs can be adaptively aligned with image-specific features via our designed query-and-fusion procedure, boosting the LLIE performance. Our proposed method can be integrated into any LLIE framework, improving both enhancement quality and downstream task performance. Extensive experiments on various benchmarks demonstrate the effectiveness of our approach.