← 🧠 Foundations of LLMs & GenAI
Core
RLHF (Alignment)
RLHF aligns a model to human preferences in three stages: supervised fine-tuning on demonstrations, training a reward model from human comparisons of outputs, then optimizing the policy with RL against that reward while a KL penalty anchors it to the base model. It shapes behavior and tone rather than facts, and FDE loops probe it because reward hacking and the KL anchor are where deployments actually go wrong.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
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