Long-tailed data poses a significant challenge for deep learning models, which tend to prioritize accurate classification of head classes while largely neglecting tail classes. Existing techniques, such as class re-balancing, logit adjustment, and data augmentation, aim to enlarge decision regions of tail classes or achieve clear decision boundaries, leaving the robustness of decision regions under-considered. This paper proposes a simple yet effective Supervised Exploratory Learning (SEL) framework to achieve these goals simultaneously from space exploration perspectives. SEL employs the adaptive Optimal Foraging Algorithm (OFA) to generate diverse exploratory examples, integrating Class-biased Complement (CbC) for balanced class distribution and Fitness-weighted Sampling (FwS) for space exploration. Both theoretical analysis and empirical results demonstrate that SEL enhances class balance, sharpens decision boundaries, and strengthens decision regions. SEL is a plug-and-play training framework that can be seamlessly integrated into model training or classifier adjustment stages, making it highly adaptable and compatible with existing methods and facilitating further performance improvements. Extensive experiments on various long-tailed benchmarks demonstrate SEL's superiority.