Phase 3: LLM Architecture · ~90 minutes · Python
RLHF: Reward Model + PPO
SFT teaches the model to follow instructions. But it doesn't teach the model which response is BETTER. Two grammatically correct, factually accurate answers can differ enormously in helpfulness. RLHF is how you encode human judgment into the model's behavior. It's what makes Claude helpful and GPT polite.
Hiring signal: Understanding of rlhf: reward model + ppo internals
What you will learn
- Build a reward model that scores response quality from human preference pairs (chosen vs rejected)
- Implement the PPO training loop that optimizes a language model policy against the reward model with a KL penalty
- Explain why RLHF requires three models (SFT, reward, policy) and how the KL constraint prevents reward hacking
- Evaluate the effect of RLHF by comparing response quality before and after preference optimization
Introduction
Type: Build Languages: Python (with numpy) Prerequisites: Phase 10, Lesson 06 (Instruction Tuning / SFT) Time: ~90 minutes
Learning Objectives
- Build a reward model that scores response quality from human preference pairs (chosen vs rejected)
- Implement the PPO training loop that optimizes a language model policy against the reward model with a KL penalty
- Explain why RLHF requires three models (SFT, reward, policy) and how the KL constraint prevents reward hacking
- Evaluate the effect of RLHF by comparing response quality before and after preference optimization
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