Radford’s team realized that by exposing GPT to a vast array of language uses and nuances, the model itself could generate more creative responses in text. Once the initial training was done, they fine-tuned the new model using some labeled examples to get better at specific tasks. This two-step approach made GPT more flexible and less reliant on having lots of labeled examples.
