AI Model Training and Post-Training
This section dives into the core processes involved in training AI models, specifically focusing on the distinction between pre-training and post-training phases. Understanding these phases is critical for comprehending the evolution of AI capabilities and the role of human feedback in shaping model behavior.
Pre-training and Post-Training: A Fundamental Distinction
Pre-training
The pre-training phase acts as the foundation for a language model's understanding of the world. Imagine it as a vast library of information. During pre-training, the model is exposed to a massive corpus of text and code, consuming and internalizing the patterns and structures within this data.
- Key Objective: The goal is to create a model that can effectively imitate and generate content similar to what it has been trained on. This involves learning the probabilistic relationships between words, phrases, and concepts within the vast dataset.
Post-Training
While pre-training lays the groundwork, post-training is where the model's behavior is honed and refined. Think of it as specializing the model for a specific task or purpose. This process involves further training the model on a more focused dataset, typically with explicit human feedback.
- Key Objective: The focus shifts from imitating the raw data to aligning the model's behavior with human preferences and expectations. This typically involves fine-tuning the model to be more helpful, informative, and aligned with desired user interactions.
Understanding the Impact
The pre-training and post-training phases work together to shape the capabilities of AI models. Pre-training provides a foundational knowledge base, while post-training enables specialization and human alignment. This distinction is essential for appreciating the nuances of model development and understanding the evolution of AI capabilities.