What is Model Training in AI?

Training a modern AI model is a substantial engineering undertaking. The process begins with data preparation: collecting, cleaning, and structuring training examples. For a frontier language model, this means assembling trillions of tokens from web crawls, books, code repositories, and curated sources, then deduplicating, filtering for quality, and removing toxic or copyrighted content. Then comes the actual training run. The model is initialized with random weights and processes batches of training data on a cluster of GPUs. For each batch, it computes predictions, calculates loss, and updates weights via backpropagation and gradient descent. This loop runs for weeks or months. Frontier model training requires distributed training across thousands of GPUs using techniques like data parallelism, tensor parallelism, and pipeline parallelism — each handling different aspects of splitting a single training run across many devices. Hyperparameters like learning rate, batch size, and warmup schedule require careful tuning. Frequent checkpoints save model state in case of hardware failures, which are common at scale. After pretraining, models go through fine-tuning stages: supervised fine-tuning on high-quality demonstrations, then reinforcement learning from human feedback to align behavior with user intent. The cost of training a frontier model now exceeds 100 million dollars in compute alone, which is why only a handful of organizations train them. For most teams, fine-tuning a pretrained model on task-specific data is the practical reality of model training.