Glossary › Training

DPO (Direct Preference Optimization)

Simpler alternative to RLHF that skips the reward model. Directly optimizes the policy on preference data using a closed-form loss. Easier to implement, equally effective in many cases.

Where this is taught

• DPO: Direct Preference Optimization — Phase 3: LLM Architecture
• End-to-End Fine-Tuning Pipeline (Data to SFT to DPO to Serve) — Phase 2: Models, Serving & Observability
• Fine-Tuning Agents for Tool Use — Phase 8: Production Agent Infrastructure
• Constitutional AI and Self-Improvement — Phase 3: LLM Architecture
• Reward Modeling & RLHF — Phase 2: Advanced RL
• Building a Complete LLM Pipeline — Phase 4: Training LLMs

Related terms

RLHF

More in Training

Backpropagation · Batch Size · Fine-Tuning · Gradient Descent · LoRA (Low-Rank Adaptation) · RLHF · QLoRA · Learning Rate

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