We consider the problem of content-based automated tag learning. In particular, we address semantic varia- tions (sub-tags) of the tag. Each video in the training set is assumed to be associated with a sub-tag label, and we treat this sub-tag label as latent information. A latent learning framework based on LogitBoost is proposed which jointly considers both tag label and the latent sub-tag label. The latent sub-tag information is exploited in our frame- work to assist the learning of our end goal, i.e., tag predic- tion. We use the cowatch information to initialize the learn- ing process. In experiments, we show that the proposed method achieves signiﬁcantly better results over baselines on a large-scale testing video set which contains about 50 million YouTube videos.