Technical
Direct Preference Optimization(DPO)
Definition
A simpler alternative to RLHF that directly optimizes language models on human preference data without training a separate reward model.In-Depth Explanation
DPO was introduced in 2023 as a more efficient way to align LLMs with human preferences. Instead of the complex RLHF pipeline (reward model training, then reinforcement learning), DPO directly updates model weights based on preference pairs. It achieves comparable results with less compute and complexity, making alignment more accessible.
Real-World Example
Fine-tuning a model by showing pairs of responses and indicating which humans preferred, without needing a separate reward model.
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