Brightening low-light images of diverse scenes is a challenging but widely concerned task in the multimedia community. Convolutional Neural Networks (CNNs) based approaches mostly acquire the enhanced model by learning the data distribution from the specific scenes. However, these works present poor adaptability (even fail) when meeting real-world scenarios that never encountered before. To conquer it, we develop a novel bilevel learning scheme for fast adaptation to bridge the gap between low-light scenes. Concretely, we construct a Retinex-induced encoder-decoder with an adaptive denoising mechanism, aiming at covering more practical cases. Different from existing works that directly learn model parameters by using the massive data, we provide a new hyperparameter optimization perspective to formulate a bilevel learning scheme towards general low-light scenarios. This scheme depicts the latent correspondence (i.e., scene-irrelevant encoder) and the respective characteristic (i.e., scene-specific decoder) among different data distributions. Due to the expensive inner optimization, estimating the hyper-parameter gradient exactly can be prohibitive, we develop an approximate hyper-parameter gradient method by introducing the one-step forward approximation and finite difference approximation to ensure the high-efficient inference. Extensive experiments are conducted to reveal our superiority against other state-of-the-art methods. A series of analytical experiments are also executed to verify our effectiveness.