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