Post-Training, Fine-Tuning, and Adaptation

Imagine spending years mastering general medicine—studying textbooks, shadowing doctors, and observing diverse clinical practices. You’d accumulate broad and impressive knowledge. But now, imagine you decide to specialize in surgery. Your extensive general training has given you a robust foundation—but becoming an expert surgeon requires additional, specialized training. In AI, foundation models undergo a similar transformation: after an expansive pretraining phase, they must be refined through fine-tuning, enabling these generalists to become specialists.

This lesson will explore how models move from general capabilities (pretraining) to specialized skills (post-training or fine-tuning). We’ll dive deeply into how transfer learning empowers fine-tuning, examine several powerful fine-tuning techniques, take an especially detailed look at reinforcement learning from human feedback (RLHF)—illustrated with OpenAI’s ChatGPT—and briefly explore model distillation techniques for efficient deployment.

What is fine-tuning?

At its core, fine-tuning takes a pretrained model—already rich with generalized knowledge—and further trains it on a smaller, specialized dataset. Think of fine-tuning as shifting from general medical studies to specialized training in neurology: your foundational understanding dramatically shortens the pathway to mastery. Technically, fine-tuning leverages transfer learning, the powerful concept of applying insights learned from solving one task (pretraining) to quickly become proficient in another closely related task.

But why isn’t a pretrained model the final product? The reason is that pretraining gives the model a broad understanding of patterns and structures, but fine-tuning adapts that knowledge to the nuances and requirements of specific tasks. Fine-tuning bridges the gap between a model’s general capabilities and practical applications. This results in better:

• Efficiency: Leveraging pretrained knowledge significantly reduces the data, time, and computational resources required compared to training models from scratch.

• Specialization: It dramatically enhances model accuracy on specific tasks such as summarization, translation, or diagnosis.

• Practicality: Modern techniques allow fine-tuning with minimal parameter adjustments, making deployment realistic and affordable.

Before diving into techniques, let’s quickly understand transfer learning, the foundation behind fine-tuning. Transfer learning involves using knowledge gained from solving one problem (the source) to improve learning on a related problem (the target). It speeds up training, reduces the need for massive datasets, and boosts performance—particularly beneficial when task-specific data is limited.

For example, OpenAI’s GPT-3 was first pretrained on massive general text datasets. Then, by using transfer learning, it was quickly adapted (fine-tuned) to perform tasks like answering questions or writing summaries.

What is full fine-tuning?

Historically, the simplest and most common method to adapt a pretrained model has been full fine-tuning. Imagine taking a highly educated generalist—someone who already knows a lot about everything—and giving them intense, focused training on a specialized task. In full fine-tuning, every single layer and every parameter of the pretrained model gets additional training using your task-specific dataset. Essentially, you're letting the model continue its education, but this time with a laser-sharp focus on your particular task.

This approach is effective because it allows the model to fully adapt all the general patterns and knowledge it previously learned to the nuances of your specialized dataset. When you have sufficient ...