The Evolution of Supervised Learning From Data Labeling to Annotation for RLHF

In the early days of machine learning, we relied on basic binary or categorical labels such as “cat” or “dog” for images or “spam” or “not spam” for emails. While effective for simple classification tasks, this approach had limited utility for more complex learning objectives. As machine learning applications grew more advanced, the demand for richer annotation techniques with detailed metadata fields became essential across industries. Instead of simple labels, datasets began to include detailed annotations such as bounding boxes for object detection, pixel-level segmentation masks, and natural language descriptions. These advancements allowed models to learn more nuanced patterns and relationships within datasets, significantly enhancing the value of existing data assets. For example, in the automotive industry, which relies heavily on computer vision, annotations evolved to include object locations, relationships between objects, and scene descriptions.