Large Language Model Training Modern LLM training follows a systematic approach from data to deployment: Training Pipeline Overview Large Language Model training is a multi-stage process that transforms raw text data into sophisticated AI systems capable of understanding and generating human language. Core Training Stages - Data Collection & Processing: Curating massive text corpora from diverse sources - Tokenization: Converting text into numerical representations - Pre-training: Learning language patterns through next-token prediction - Post-training: Alignment with human preferences and safety constraints The Foundation: Pre-training Pre-training is the computationally intensive phase where models learn fundamental language understanding. Mathematical Foundation Next-Token Prediction Objective The core training objective is autoregressive language modeling: mathcalL = -sum_t=1^T log P(x_t | x_