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COURSE 19 OF 21

Post-training and Alignment

Learn how demonstrations, preference rankings, and carefully constrained updates turn a pretrained model into an assistant.

Background reading
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Introduction to Post-training and Alignment

Post-training continues training after pretraining so a general language model follows instructions and behaves in ways people prefer. Alignment asks whether those learned behaviors match the intended goals and constraints.

Pretraining teaches a model to continue text. Post-training changes that general text predictor into a model that responds to instructions in a preferred way.

These stages optimize different signals, so they must be evaluated separately. Human preference, instruction following, truthfulness, toxicity, and retained task ability can move in different directions.

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What you will learn next

  1. 01Supervised Fine-Tuning (SFT)
  2. 02Reward Models and Reinforcement Learning From Human Feedback (RLHF)
  3. 03Direct Preference Optimization (DPO)
  4. 04Low-Rank Adaptation Changes Which Weights Are Trained
  5. 05Evaluation and Alignment Regressions
  6. 06Choosing Between Demonstrations and Preference Data
  7. 07Reading One Preference Pair
  8. 08Review
  9. 09References