What does "Two-Stage Training" mean?
Table of Contents
Two-Stage Training is a method used to improve how models learn and make predictions. This approach breaks the learning process into two separate steps, allowing each step to focus on specific tasks.
Step One: Initial Learning
In the first stage, the model learns basic information from a large set of data. This helps the model understand the general patterns and features of the data it will work with later. For instance, in tasks like understanding images or text, the model gathers essential details without worrying about the final outcome just yet.
Step Two: Fine-Tuning
In the second stage, the model refines its skills based on what it learned in the first step. It focuses on improving its accuracy and performance by using more specific data or feedback. This fine-tuning process helps the model make better predictions or generate more relevant results.
Benefits
Using Two-Stage Training can lead to better results in various tasks. It allows the model to learn effectively while avoiding confusion that can arise when trying to learn everything at once. This method has been shown to enhance performance in areas like image classification and code generation, making models smarter and more reliable.