Phase 3: LLM Architecture · ~90 minutes · Python
DPO: Direct Preference Optimization
RLHF works. It also requires training three models (SFT, reward model, policy), managing PPO's instability, and tuning a KL penalty. DPO asks: what if you could skip all of that? DPO directly optimizes the language model on preference pairs. No reward model. No PPO. One training loop. Same results.
Hiring signal: Understanding of dpo: direct preference optimization internals
What you will learn
- Implement DPO training that directly optimizes a language model on preference pairs without a separate reward model
- Derive the DPO loss function and explain how it implicitly represents a reward model through the policy's log probabilities
- Compare DPO vs RLHF in terms of training stability, compute cost, and number of models required
- Tune the beta parameter to control how far the trained policy diverges from the reference model
Introduction
Type: Build Languages: Python (with numpy) Prerequisites: Phase 10, Lesson 07 (RLHF) Time: ~90 minutes
Learning Objectives
- Implement DPO training that directly optimizes a language model on preference pairs without a separate reward model
- Derive the DPO loss function and explain how it implicitly represents a reward model through the policy's log probabilities
- Compare DPO vs RLHF in terms of training stability, compute cost, and number of models required
- Tune the beta parameter to control how far the trained policy diverges from the reference model
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