- 了解监督学习和强化学习的基础知识
- 具备模型微调和神经网络架构的经验
- 熟悉Python编程和深度学习框架（例如TensorFlow，PyTorch）
受众
- Machine Learning工程师
- AI研究人员
Reinforcement Learning 来自人类反馈的强化学习（RLHF）是一种尖端方法，用于微调如 ChatGPT 及其他顶级 AI 系统的模型。
这项由讲师指导的培训（线上或线下）针对高阶机器学习工程师和 AI 研究人员，他们希望应用 RLHF 来微调大型 AI 模型，以实现卓越的性能、安全性和对齐性。
在培训结束时，参与者将能够：
- 理解 RLHF 的理论基础，以及它在现代 AI 开发中的重要性。
- 基于人类反馈实现奖励模型，以指导强化学习过程。
- 使用 RLHF 技术微调大型语言模型，使其输出与人类偏好一致。
- 应用最佳实践来扩展 RLHF 工作流程，以适用于生产级 AI 系统。
课程形式
- 互动式讲座与讨论。
- 大量练习与实践。
- 在即时实验环境中进行动手实作。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
人类反馈强化学习（RLHF）简介
- 什么是RLHF及其重要性
- 与监督微调方法的比较
- RLHF在现代AI系统中的应用
基于人类反馈的奖励建模
- 收集与结构化人类反馈
- 建立与训练奖励模型
- 评估奖励模型的有效性
使用近端策略优化（PPO）进行训练
- RLHF中的PPO算法概述
- 使用奖励模型实现PPO
- 迭代与安全地微调模型
语言模型的实际应用
- 为RLHF工作流程准备数据集
- 使用RLHF进行小型LLM的实操微调
- 挑战与缓解策略
将RLHF扩展至生产系统
- 基础设施与计算考量
- 质量保证与持续反馈循环
- 部署与维护的最佳实践
伦理考量与偏见缓解
- 解决人类反馈中的伦理风险
- 偏见检测与校正策略
- 确保对齐与安全输出
案例研究与实际范例
- 案例研究：使用RLHF微调模型
- 其他成功的RLHF部署
- 经验教训与行业洞察
总结与下一步
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