使用直接偏好最佳化 (DPO) 方法對 LLM 進行偏好微調 (Preference Tuning)
Original: Preference Tuning LLMs with Direct Preference Optimization Methods
This technical blog post from Hugging Face takes an in-depth look at the latest techniques in "preference tuning," with a particular focus…
本指南介紹了如何利用 Hugging Face 的 TRL 函式庫進行 LLM 的偏好微調。傳統的 RLHF 需要訓練獎勵模型並使用複雜的 PPO 演算法，而 DPO（直接偏好最佳化）及其變體（IPO、KTO）能直接在偏好數據上進行訓練，大幅簡化了對齊流程。文章詳細說明了這些方法的原理、數據格式要求以及實際程式碼實作。
This technical blog post from Hugging Face takes an in-depth look at the latest techniques in "preference tuning," with a particular focus on **Direct Preference Optimization (DPO)** and its derivative methods.
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