Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF) Training Course
Reinforcement Learning from Human Feedback (RLHF) represents a state-of-the-art approach for optimising models such as ChatGPT and other leading AI systems.
This instructor-led, live training (available online or onsite) is designed for senior machine learning engineers and AI researchers seeking to utilise RLHF to fine-tune large-scale AI models, thereby enhancing performance, safety, and alignment.
Upon completion, participants will be equipped to:
- Grasp the theoretical underpinnings of RLHF and its critical role in contemporary AI development.
- Develop reward models driven by human feedback to steer reinforcement learning processes.
- Fine-tune large language models via RLHF techniques to ensure outputs align with human preferences.
- Implement best practices for scaling RLHF workflows to support production-grade AI systems.
Course Delivery Format
- Interactive lectures and group discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customisation Options
- To request a tailored training experience for this course, please reach out to us to make arrangements.
Course Outline
Introduction to Reinforcement Learning from Human Feedback (RLHF)
- Defining RLHF and its significance
- Contrasting RLHF with supervised fine-tuning methods
- Applications of RLHF in modern AI systems
Reward Modelling with Human Feedback
- Gathering and structuring human feedback data
- Constructing and training reward models
- Assessing the effectiveness of reward models
Training via Proximal Policy Optimization (PPO)
- Overview of PPO algorithms in the context of RLHF
- Implementing PPO alongside reward models
- Iterative and safe fine-tuning of models
Practical Fine-Tuning of Language Models
- Preparing datasets for RLHF workflows
- Hands-on fine-tuning of a smaller LLM using RLHF
- Identifying challenges and applying mitigation strategies
Scaling RLHF for Production Systems
- Infrastructure and computational resource considerations
- Quality assurance and establishing continuous feedback loops
- Best practices for deployment and ongoing maintenance
Ethical Considerations and Bias Mitigation
- Managing ethical risks associated with human feedback
- Strategies for detecting and correcting bias
- Ensuring alignment and safety of model outputs
Case Studies and Real-World Applications
- Case study: Fine-tuning ChatGPT using RLHF
- Other successful RLHF implementations
- Key lessons learned and industry insights
Requirements
- A solid understanding of supervised and reinforcement learning fundamentals
- Practical experience with model fine-tuning and neural network architectures
- Proficiency in Python programming and deep learning frameworks (e.g., TensorFlow, PyTorch)
Target Audience
- Machine learning engineers
- AI researchers
Open Training Courses require 5+ participants.
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