A new framework named Controllable Pseudo-label Generation (CPG) has been proposed to advance the field of long-tailed semi-supervised learning. This approach specifically addresses the challenge posed by unknown distributions in unlabeled data, which can hinder the effectiveness of traditional learning methods. By generating pseudo-labels that are adaptable to various data distributions, CPG aims to improve the accuracy and robustness of semi-supervised learning models. The framework’s design allows it to better handle the imbalance and diversity inherent in long-tailed datasets. According to recent research published on arXiv, CPG shows promise in enhancing learning outcomes by controlling the pseudo-label generation process. This development aligns with ongoing efforts to refine semi-supervised learning techniques for more realistic and practical applications. The introduction of CPG marks a significant step toward more flexible and effective machine learning models in scenarios with limited labeled data.