Pre-training
Also known as: Pretraining
Self-supervised next-token training on a huge unlabeled corpus, producing the base model.
The initial self-supervised phase where a model learns from a massive unlabeled corpus, typically via next-token prediction. Produces a 'base model' that's a strong generic predictor but not yet aligned with human preferences.
In practice
The foundation everything else builds on. Senior interviews probe data mixture, curriculum, and the post-train (SFT + RLHF) stack.
How it compares
Pre-training is from-scratch on massive generic data; fine-tuning adapts a pre-trained model on a smaller curated set.
Related topics
Related terms
Fine-tuning
Continue training a pre-trained model on task-specific data to specialize its behavior.
RLHF (Reinforcement Learning from Human Feedback)
Train a reward model from human preference pairs, then RL-fine-tune the LLM against that reward.
LoRA (Low-Rank Adaptation)
Fine-tune by training small low-rank update matrices instead of every weight: same quality, ~1% of the cost.
DPO (Direct Preference Optimization)
Skip the reward model and PPO: fine-tune directly on preferred-vs-rejected response pairs.
Knowledge Distillation
Train a small student model to match a big teacher's outputs: cheap, fast inference with most of the quality.
Reasoning Model
An LLM trained to reason at length internally before answering. Slower and more expensive, but much better on hard problems.