LLM Training Explained: Pre-Training vs. Post-Training with Julien Launay

The Two Phases of LLM Training

• 💡 Historically, LLM creation involved two main phases: pre-training and post-training.
• 🎯 Pre-training is the most computationally intensive phase, involving training on vast amounts of text and data from the web, books, and papers.
• 🚀 The primary goal of pre-training is to train the model to predict the next word or token, enabling it to learn language patterns at scale.

Challenges with Pure Pre-Training

• ⚠️ Models that undergo only pre-training can be unwieldy and not very interactive.
• 💬 A common failure mode is when a pre-trained model, asked a question, instead generates more questions similar to the prompt, as this pattern is equally likely in its training data.
• 🧩 This limitation led to the development of a second phase to refine the model's behavior.

The Role of Post-Training

• 🛠️ Post-training aims to sharpen the model and align it with its intended use, such as becoming a helpful chat assistant.
• 🧠 While post-training can involve continued pre-training on specialized data (like chat transcripts), a major success has been the use of reinforcement learning.
• 👍 Reinforcement learning allows models to learn from feedback (e.g., human ratings like thumbs up/down) rather than just explicit demonstrations, guiding the model to improve its responses.

Blurring Lines in Modern LLM Training

• 📈 The distinction between pre-training and post-training is becoming less rigid, with pre-training now incorporating dynamic data distributions and higher-quality data as it progresses.
• 🔄 Some approaches are beginning to integrate reinforcement learning techniques directly into the pre-training phase.
• 💰 There's a significant trend of scaling up post-training, with recent models spending nearly as much on post-training as on pre-training, a substantial shift from historical practices.