Post-training

Post-training refers to the methods applied to a machine learning or large language model after the initial pre-training phase to enhance its performance, usability, and reliability. While pre-training provides the foundational knowledge by training on large-scale datasets, post-training adapts the model to specific goals, tasks, or ethical standards.

Common post-training techniques include:

• Supervised fine-tuning (SFT): training on curated, labeled datasets to improve task-specific performance.
• Reinforcement learning from human feedback (RLHF): aligning model outputs with human preferences through feedback-driven optimization.
• Knowledge distillation: transferring knowledge from a larger model to a smaller, more efficient one.
• Instruction tuning: training the model to better follow natural language instructions.
• Domain adaptation: refining the model with domain-specific datasets (e.g., medical, legal, or technical content).

For AEO and AI-powered search, post-training is crucial because it helps models interpret user intent, cite authoritative sources, and provide reliable, high-quality responses. Alignment steps in post-training directly affect how models handle bias, safety, and factual accuracy.