How are large language models trained?
Large Language Models (LLMs) undergo two core training phases: pre-training and post-training. Pre-training uses next token prediction on massive datasets to build foundational language knowledge. Post-training refines this model for safety, helpfulness, and instruction following using techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL). The process requires complex engineering at scale, including managing petabytes of data and utilizing advanced evaluation methods like auto-raters.
Key takeaways
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Pre-training: Foundational Knowledge
The initial phase trains the model using next token prediction (a self-supervised task). This allows the LLM to implicitly learn language patterns and world knowledge without manual labeling. Scaling this process is a massive engineering challenge, requiring complex parallelism across thousands of interconnected GPUs, automated recovery systems, and pipelines for petabytes of data.
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Post-training: Alignment and Refinement
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This phase focuses on aligning the model to be safe, helpful, and accurate. It primarily involves Supervised Fine-Tuning (SFT) using gold standard examples, or Reinforcement Learning (RL), where a separate Reward Model grades responses for quality, pushing the LLM toward desired behaviors.
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Evaluation and Measurement
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Measuring model success requires defining clear criteria. While pre-training measures next word prediction accuracy, post-training focuses on alignment (e.g., helpfulness). Evaluation is scaled using a second LLM—an auto rater—to score responses against defined criteria.
