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
• Fine-Tuning Agents for Tool Use — Phase 8: Production Agent Infrastructure
• Building a Complete LLM Pipeline — Phase 4: Training LLMs
• Reward Modeling & RLHF — Phase 2: Advanced RL
• Constitutional AI and Self-Improvement — Phase 3: LLM Architecture

Related terms

RLHF

More in Training

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

Learn this by building

DeVenture Academy teaches AI engineering through projects — every lesson is paired with a hands-on lab you run on your own machine with real tools.

Create a free account — the opening phases of 24 of 30 courses are free, no credit card. Or see Pro pricing.

All courses · Pricing · About · FAQ · Glossary