Large language models (LLMs) are trained through a multi-stage process that combines unsupervised pre-training, supervised fine-tuning, and reinforcement learning. The goal is to build a model that understands language patterns and can generate coherent, context-aware text. Here’s how it works in practice.
Pre-training: Learning Language Patterns The first stage involves pre-training on vast amounts of unstructured text data, such as books, websites, and articles. The model learns to predict the next word in a sequence (autoregressive training) or fill in missing words (masked language modeling). For example, given the input “The sky is ___,” the model might predict “blue.” This is done using transformer architectures, which process text in parallel using self-attention mechanisms to weigh relationships between words. Tokenization breaks text into smaller units (e.g., subwords like “un” + “breakable”), allowing the model to handle rare words. Training involves optimizing parameters via gradient descent to minimize prediction errors across billions of examples. For instance, GPT-3 was trained on roughly 45 terabytes of text data, requiring weeks of computation on thousands of GPUs.
Fine-tuning: Adapting to Specific Tasks After pre-training, the model is fine-tuned on smaller, task-specific datasets to improve performance for applications like chatbots or code generation. For example, a model might be trained on question-answer pairs (e.g., “What’s Python? → A programming language…”) to improve accuracy. This stage often uses supervised learning, where labeled data guides the model’s outputs. Reinforcement learning from human feedback (RLHF) is also common: humans rank model responses (e.g., preferring concise answers over verbose ones), and the model adjusts its behavior to maximize these rankings. Tools like Proximal Policy Optimization (PPO) are used here, aligning outputs with human preferences without requiring explicit labeled data for every scenario.
Infrastructure and Optimization Training LLMs demands significant computational resources. Distributed training frameworks like TensorFlow or PyTorch split workloads across GPU/TPU clusters. For example, a model with 175 billion parameters (like GPT-3) might use model parallelism to split layers across devices, while data parallelism processes batches simultaneously. Memory optimization techniques (e.g., gradient checkpointing) reduce hardware constraints. Once trained, techniques like quantization shrink the model for deployment. The entire pipeline—pre-training, fine-tuning, and optimization—requires careful balancing of data, algorithms, and hardware to achieve practical results.