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Direct Preference Optimization(DPO)
Offline preference optimization with a classification loss over chosen and rejected responses, without training an explicit reward model
In 30 seconds
- What
- Trains a policy directly on preference pairs by minimizing a classification loss that compares chosen versus rejected responses relative to a frozen reference policy, skipping explicit reward modeling.
- When to use
- You have paired preference data (chosen and rejected outputs) and want to align behavior without the overhead of training a separate reward model first.
- Watch out
- Policy can drift far from the reference, degrading performance on tasks outside the preference dataset or causing mode collapse if preference pairs are narrow.
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Direct Preference Optimization: Overview
Offline preference optimization with a classification loss over chosen and rejected responses, without training an explicit reward model
- Chosen and rejected response pairs
- Frozen reference policy comparison
- Direct policy optimization
- Log-ratio classification objective
- Offline preference training
- Behavior-drift monitoring
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References
The papers, specifications, and repositories this pattern is based on.
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