Reinforcement Learning from Human Feedback (RLHF) How AI isPublished 6/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English |
Duration: 3h 40m |
Size: 2.74 GB
Examine the theoretical frameworks and training loops used to align raw neural models with human preferences, va...
What you'll learnMaster the core principles of Reward Modeling.
Deconstruct the architecture and tradeoffs of Proximal Policy Optimization (PPO).
Analyze the design patterns governing Direct Preference Optimization (DPO).
Build a deep mental model of Alignment Drift at scale.
RequirementsNo coding experience is required. We focus entirely on system design and core theoretical concepts.
A basic interest in technology systems, algorithms, or computer science architecture.
No special software or local development environment setup is needed.
Description"This course contains the use of artificial intelligence."
Master the Theory Behind AI Alignment - No Programming Required
Modern AI systems don't become helpful, honest, and safe by chance-they are carefully aligned with human preferences using Reinforcement Learning from Human Feedback (RLHF). This course provides a comprehensive, mathematics-driven understanding of the techniques that enable Large Language Models to generate responses that better match human expectations.
Unlike coding-focused courses, this program emphasizes the theoretical foundations, mathematical intuition, architectural design, and decision-making principles behind RLHF. You'll gain a deep conceptual understanding of how alignment systems are built without writing a single line of code.
Whether you're an AI engineer, researcher, product manager, or simply curious about how models like ChatGPT are trained, this course equips you with the knowledge to understand one of the most important breakthroughs in modern Artificial Intelligence.
What you'll learn- Build a solid mathematical foundation in reinforcement learning, optimization, probability, and policy learning.
- Understand why AI alignment is essential for modern Large Language Models.
- Learn how human feedback is collected, processed, and transformed into training signals.
- Explore Reward Modeling and how preference data is converted into reward functions.
- Master the principles of Proximal Policy Optimization (PPO) and why it became the standard RLHF optimization algorithm.
- Understand Direct Preference Optimization (DPO) and how it simplifies preference-based learning.
- Learn about Alignment Drift, reward hacking, and distribution shifts in deployed AI systems.
- Analyze the computational, memory, and scalability trade-offs of RLHF pipelines.
- Study ethical AI, explainability, model auditing, and governance frameworks.
- Identify common alignment failures and architectural anti-patterns in modern AI systems.
Course Curriculum
Module 1: Mathematical Foundations
- Linear Algebra
- Probability & Statistics
- Optimization Fundamentals
- Gradient-Based Learning
- Mathematical Foundations of Reinforcement Learning
Module 2: Reinforcement Learning Fundamentals
- Markov Decision Processes (MDPs)
- Policies and Value Functions
- Rewards and Returns
- Exploration vs Exploitation
- Policy Optimization Concepts
Module 3: Foundations of AI Alignment
- Why AI Alignment Matters
- Human Preference Learning
- Alignment Objectives
- Safety Challenges in Large Language Models
- Alignment Pipeline Overview
Module 4: Reward Modeling
- Human Preference Collection
- Pairwise Ranking
- Reward Function Learning
- Preference Dataset Construction
- Reward Model Evaluation
Module 5: Proximal Policy Optimization (PPO)
- PPO Intuition
- Policy Updates
- Clipped Objective Function
- Stable Reinforcement Learning
- Practical RLHF Optimization
Module 6: Direct Preference Optimization (DPO)
- Motivation Behind DPO
- Preference-Based Learning
- Mathematical Foundations
- Comparison with PPO
- Modern Alignment Strategies
Module 7: Alignment Challenges
- Alignment Drift
- Distribution Shift
- Reward Hacking
- Robustness
- Long-Term Model Behavior
Module 8: Architecture & System Trade-offs
- Compute vs Performance
- Memory Considerations
- Latency Optimization
- Scalability
- Production AI Systems
Module 9: Explainability & AI Governance
- Explainable AI (XAI)
- Model Auditing
- Fairness and Bias
- Responsible AI
- Governance Frameworks
Module 10: Future of AI Alignment
- Constitutional AI
- Preference Optimization Techniques
- Human-AI Collaboration
- Emerging Alignment Research
- Future Directions in Safe Artificial Intelligence
Why Take This Course?
- No programming or coding experience required
- Strong focus on mathematical intuition and conceptual understanding
- Covers the complete RLHF pipeline used in modern Large Language Models
- Learn the theory behind ChatGPT-style AI alignment
- Ideal for AI professionals, researchers, architects, and technical leaders
- Gain a long-lasting understanding of AI alignment principles rather than implementation-specific tools
Who this course is forML Engineers, Product Managers, AI Safety Researchers
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