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In recent years, deep learning has achieved a great success in various image recognition tasks. However, the long-tailed setting over a semantic class plays a leading role in real-world applications. Common methods focus on optimization on balanced distribution or naive models. Few works explore long-tailed learning from a deep learning-based generalization perspective. The loss landscape on long-tailed learning is first investigated in this work. Empirical results show that sharpness-aware optimizers work not well on long-tailed learning. Because they do not take class priors into consideration, and they fail to improve performance of few-shot classes. To better guide the network and explicitly alleviate sharpness without extra computational burden, we develop a universal Balanced Gradient Penalty (BGP) method. Surprisingly, our BGP method does not need the detailed class priors and preserves privacy. Our …