Next-Token Prediction

Next-Token Prediction is a fundamental training objective for language models that learns to predict the most likely subsequent token in a sequence given all preceding tokens as context. This autoregressive approach enables models to develop comprehensive understanding of language patterns, syntax, semantics, and world knowledge through self-supervised learning on large text corpora. The technique operates by masking future tokens during training, forcing models to predict each token based solely on leftward context, creating a natural learning signal without requiring labeled data. Next-token prediction serves as the foundation for most modern large language models, enabling capabilities like text generation, conversation, and reasoning through iterative token sampling. Advanced implementations incorporate techniques like teacher forcing, scheduled sampling, and contrastive learning to optimize prediction accuracy and reduce exposure bias. This training paradigm allows models to learn complex linguistic structures, factual knowledge, and reasoning patterns that emerge from statistical regularities in training data.