Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF) Training Course
Reinforcement Learning from Human Feedback (RLHF) is a cutting-edge method used for fine-tuning models like ChatGPT and other top-tier AI systems.
This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to apply RLHF to fine-tune large AI models for superior performance, safety, and alignment.
By the end of this training, participants will be able to:
- Understand the theoretical foundations of RLHF and why it is essential in modern AI development.
- Implement reward models based on human feedback to guide reinforcement learning processes.
- Fine-tune large language models using RLHF techniques to align outputs with human preferences.
- Apply best practices for scaling RLHF workflows for production-grade AI systems.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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Course Outline
Introduction to Reinforcement Learning from Human Feedback (RLHF)
- What is RLHF and why it matters
- Comparison with supervised fine-tuning methods
- RLHF applications in modern AI systems
Reward Modeling with Human Feedback
- Collecting and structuring human feedback
- Building and training reward models
- Evaluating reward model effectiveness
Training with Proximal Policy Optimization (PPO)
- Overview of PPO algorithms for RLHF
- Implementing PPO with reward models
- Fine-tuning models iteratively and safely
Practical Fine-Tuning of Language Models
- Preparing datasets for RLHF workflows
- Hands-on fine-tuning of a small LLM using RLHF
- Challenges and mitigation strategies
Scaling RLHF to Production Systems
- Infrastructure and compute considerations
- Quality assurance and continuous feedback loops
- Best practices for deployment and maintenance
Ethical Considerations and Bias Mitigation
- Addressing ethical risks in human feedback
- Bias detection and correction strategies
- Ensuring alignment and safe outputs
Case Studies and Real-World Examples
- Case study: Fine-tuning ChatGPT with RLHF
- Other successful RLHF deployments
- Lessons learned and industry insights
Summary and Next Steps
Requirements
- An understanding of supervised and reinforcement learning fundamentals
- Experience with model fine-tuning and neural network architectures
- Familiarity with Python programming and deep learning frameworks (e.g., TensorFlow, PyTorch)
Audience
- Machine learning engineers
- AI researchers
14 Hours
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