Course Code: ftrlhf
Duration: 14 hours
Prerequisites:
  • 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
Overview:

Reinforcement Learning 來自人類反饋的強化學習(RLHF)是一種尖端方法,用於微調如 ChatGPT 及其他頂級 AI 系統的模型。

這項由講師指導的培訓(線上或線下)針對高階機器學習工程師和 AI 研究人員,他們希望應用 RLHF 來微調大型 AI 模型,以實現卓越的性能、安全性和對齊性。

在培訓結束時,參與者將能夠:

  • 理解 RLHF 的理論基礎,以及它在現代 AI 開發中的重要性。
  • 基於人類反饋實現獎勵模型,以指導強化學習過程。
  • 使用 RLHF 技術微調大型語言模型,使其輸出與人類偏好一致。
  • 應用最佳實踐來擴展 RLHF 工作流程,以適用於生產級 AI 系統。

課程形式

  • 互動式講座與討論。
  • 大量練習與實踐。
  • 在即時實驗環境中進行動手實作。

課程定制選項

  • 如需為本課程定制培訓,請聯繫我們安排。
Course Outline:

人類反饋強化學習(RLHF)簡介

  • 什麼是RLHF及其重要性
  • 與監督微調方法的比較
  • RLHF在現代AI系統中的應用

基於人類反饋的獎勵建模

  • 收集與結構化人類反饋
  • 建立與訓練獎勵模型
  • 評估獎勵模型的有效性

使用近端策略優化(PPO)進行訓練

  • RLHF中的PPO算法概述
  • 使用獎勵模型實現PPO
  • 迭代與安全地微調模型

語言模型的實際應用

  • 為RLHF工作流程準備數據集
  • 使用RLHF進行小型LLM的實操微調
  • 挑戰與緩解策略

將RLHF擴展至生產系統

  • 基礎設施與計算考量
  • 質量保證與持續反饋循環
  • 部署與維護的最佳實踐

倫理考量與偏見緩解

  • 解決人類反饋中的倫理風險
  • 偏見檢測與校正策略
  • 確保對齊與安全輸出

案例研究與實際範例

  • 案例研究:使用RLHF微調模型
  • 其他成功的RLHF部署
  • 經驗教訓與行業洞察

總結與下一步

Sites Published:

United Arab Emirates - Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

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