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Direct Preference Optimization (DPO)

Description

Direct Preference Optimization (DPO) is a fine-tuning method for aligning large language models (LLMs) with human preferences, without the need for reinforcement learning or reward modeling. DPO directly optimizes the model to prefer outputs that are rated higher by humans, using pairs of preferred and less-preferred responses.

Info

DPO is designed to be more efficient and easier to implement than RLHF, as it avoids the complexity of training a separate reward model and reinforcement learning loop.

Workflow

  1. Collect Preference Data: Gather pairs of model outputs for the same prompt, labeled as "preferred" and "less preferred" by humans.
  2. Initialize Model: Start with a pretrained language model.
  3. Fine-Tune with DPO Loss: Train the model using a loss function that increases the likelihood of preferred responses over less-preferred ones.
  4. Evaluate: Assess alignment and performance on preference-based benchmarks.

Warning

DPO requires high-quality preference data. Poorly labeled or inconsistent preferences can degrade model alignment.