Post-training involves techniques and modifications applied after an initial model training phase to enhance performance, adjust parameters, or optimize computational efficiency. Examples include fine-tuning on specific data sets to improve task-specific accuracy or applying pruning methods to reduce model size for faster inference. It's closely related to concepts like transfer learning and model optimization, which focus on improving pre-trained models for specific applications.