The essence of GPT-1's downstream fine-tuning strategy is "maximal reuse, minimal modification": unified model architecture, where almost all parameters come from the pre-trained model, and only a very small number of new parameters (the output layer Wy and the delimiter embeddings) are introduced during the fine-tuning stage; unified input format, where, through serialized transformation, different tasks can all be processed by the same model without needing to design complex custom architectures for each task; enhanced objective function, where, by incorporating a language model auxiliary loss, the balance between task-specific learning and general knowledge retention is effectively maintained.