跳到主要内容Skip to main content

训练三阶段SFT → Reward Model → RL

SFT → Reward Model → RL

  • 核心概念Core concepts
  • 第四站 · 看懂Station 4 · Under the Hood
解决什么问题What problem it solves

聊天模型是怎么练成的:先会续写,再学会好好回答。How chat models are trained: first they learn to continue text, then they learn to answer well.

这是什么What it is

第一阶段是预训练:海量文本上学续写。

第二阶段训练奖励模型:人给多个回答排序,模型学会判断好坏。

第三阶段是强化学习:按奖励模型的打分继续优化。

Stage one is pretraining: learning to continue text from massive corpora.

Stage two trains a reward model: humans rank candidate answers, and a model learns to judge quality.

Stage three is reinforcement learning: keep optimizing against the reward model's scores.

为什么需要知道Why it matters

ChatGPT 一类模型的「听话」来自后两阶段,不是预训练。较新的 DPO 把后两阶段合并成一步,不用单独训奖励模型。What makes ChatGPT-style models "follow instructions" comes from stages two and three, not pretraining. The newer DPO merges the last two stages into one, with no separate reward model.

出处Sources
  • InstructGPT 论文(三阶段见 §3.1)InstructGPT paper (three stages, §3.1) ↗
  • DPO 论文(免 RL 的对齐方法)Direct Preference Optimization (paper) ↗
  • 从业者深版:SFT 与偏好对齐专题(AI 模型站)Practitioner deep-dives: SFT and alignment (AI Models site) ↗