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RLHF

Reinforcement Learning from Human Feedback. Process: (1) collect human preference comparisons, (2) train reward model, (3) optimize LLM policy via PPO/DPO. Aligns model outputs with human preferences.

Where this is taught

• RLHF & Alignment Evaluation — Phase 2b: LLM-Specific Responsible AI
• Reward Modeling & RLHF — Phase 2: Advanced RL
• RLHF: Reward Model + PPO — Phase 3: LLM Architecture
• Reinforcement Learning: Learning from Reward, Not Labels — Phase 2: Classical Machine Learning
• Labeling, Annotation & Human Feedback — Phase 3: Data Strategy for AI Products
• DPO: Direct Preference Optimization — Phase 3: LLM Architecture

Related terms

DPO

More in Training

Backpropagation · Batch Size · Fine-Tuning · Gradient Descent · LoRA (Low-Rank Adaptation) · DPO (Direct Preference Optimization) · QLoRA · Learning Rate

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