Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This 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. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
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.
📋 Course Objectives
1. Translate AI Enablement theory into practical, reproducible analysis.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• Tangible, reproducible AI Enablement work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
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
Data Collection & Annotation
Design crowdsourcing workflows for preference data • Apply quality‑control techniques and bias mitigation • Curate industrial datasets for RLHF experiments
Reward Modeling
Train reward models from human preferences • Validate reward signals with offline evaluation • Debug reward mis‑specification issues
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
Evaluation, Safety, and Alignment
Design automated and human‑in‑the‑loop evaluation metrics • Detect and mitigate harmful behaviors • Prepare audit reports for compliance
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
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | OpenAI Gym |
| Covered Tool / Platform | Hugging Face Transformers |
| Covered Tool / Platform | trl |
| Covered Tool / Platform | DPO |
| Covered Tool / Platform | Weights & Biases |
| Covered Tool / Platform | Cloud GPU |
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