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update dpo pynb #181

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Aug 27, 2023
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update dpo pynb #181

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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -52,7 +52,7 @@ Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning) and DPO(
- 第一阶段：PT(Continue PreTraining)增量预训练，在海量领域文档数据上二次预训练GPT模型，以注入领域知识（可选）
- 第二阶段：SFT(Supervised Fine-tuning)有监督微调，构造指令微调数据集，在预训练模型基础上做指令精调，以对齐指令意图
- 第三阶段
- RLHF(Reinforcement Learning from Human Feedback)基于人类反馈对语言模型进行强化学习，分为两步：1）RM(Reward Model)奖励模型建模，构造人类偏好排序数据集，训练奖励模型，用来建模人类偏好，主要是"HHH"原则，具体是"helpful, honest, harmless"；2）RL(Reinforcement Learning)强化学习，用奖励模型来训练SFT模型，生成模型使用奖励或惩罚来更新其策略，以便生成更高质量、更符合人类偏好的文本
- RLHF(Reinforcement Learning from Human Feedback)基于人类反馈对语言模型进行强化学习，分为两步：1）RM(Reward Model)奖励模型建模，构造人类偏好排序数据集，训练奖励模型，用来建模人类偏好，主要是"HHH"原则，具体是"helpful, honest, harmless"； 2）RL(Reinforcement Learning)强化学习，用奖励模型来训练SFT模型，生成模型使用奖励或惩罚来更新其策略，以便生成更高质量、更符合人类偏好的文本
- [DPO(Direct Preference Optimization)](https://arxiv.org/pdf/2305.18290.pdf)直接偏好优化方法，DPO通过直接优化语言模型来实现对其行为的精确控制，而无需使用复杂的强化学习，也可以有效学习到人类偏好，DPO相较于RLHF更容易实现且易于训练，效果更好


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