| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Advanced |
| Duration | 4 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹2499 / $59 |
| Tools | Python PyTorch OpenAI Gym Hugging Face Transformers trl DPO Weights & Biases Cloud GPU |
About the RLHF (Reinforcement Learning from Human Feedback) Course
Reinforcement Learning from Human Feedback (RLHF) equips you with the theory, algorithms, and practical pipelines to align powerful language and decision models with human values.
Over four intensive weeks you will design reward models, collect human preferences, and fine‑tune agents on real‑world tasks, preparing you for cutting‑edge AI research and product development.
Program Highlights
• Comprehensive coverage of RLHF (Reinforcement Learning from Human Feedback) from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Artificial Intelligence
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: Python, PyTorch, OpenAI Gym, Hugging Face Transformers
• Career-oriented training for academic and professional growth in Artificial Intelligence
Course Curriculum
Module 1: Foundations of RL & Human Feedback
- Understand core RL concepts and Markov decision processes
- Explore human feedback mechanisms and preference learning
- Implement baseline RL agents in Python
Module 2: Data Collection & Annotation
- Design crowdsourcing workflows for preference data
- Apply quality‑control techniques and bias mitigation
- Curate industrial datasets for RLHF experiments
Module 3: Reward Modeling
- Train reward models from human preferences
- Validate reward signals with offline evaluation
- Debug reward mis‑specification issues
Module 4: Policy Optimization with Human Feedback
- Apply Proximal Policy Optimization (PPO) with reward models
- Integrate KL‑regularization for safe fine‑tuning
- Scale training on GPU clusters
Module 5: Evaluation, Safety, and Alignment
- Design automated and human‑in‑the‑loop evaluation metrics
- Detect and mitigate harmful behaviors
- Prepare audit reports for compliance
Module 6: Capstone Project
- Define a real‑world RLHF use‑case
- Build end‑to‑end pipeline from data collection to deployment
- Present findings and receive mentor feedback
Tools, Techniques, or Platforms Covered
Python PyTorch OpenAI Gym Hugging Face Transformers trl DPO Weights & Biases Cloud GPU
Real-World Applications
- Apply RLHF (Reinforcement Learning from Human Feedback) skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Artificial Intelligence competencies
- Solve industry-relevant problems using RLHF (Reinforcement Learning from Human Feedback) methodologies and tools
- Contribute to open-source projects and collaborative research in Artificial Intelligence
- Prepare for competitive examinations, interviews, and professional certifications in Artificial Intelligence
Who Should Attend & Prerequisites
- Industry‑recognised e‑Certification + e‑Marksheet from NSTC
- Hands‑on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
