Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF) Training Course
Reinforcement Learning from Human Feedback (RLHF) represents a state-of-the-art technique employed to fine-tune models such as ChatGPT and other leading AI systems.
This instructor-led live training, available either online or onsite, is designed for advanced machine learning engineers and AI researchers aiming to utilise RLHF to fine-tune large AI models for enhanced performance, safety, and alignment.
Upon completing this training, participants will be equipped to:
- Grasp the theoretical underpinnings of RLHF and appreciate its critical role in contemporary AI development.
- Develop reward models leveraging human feedback to steer reinforcement learning processes.
- Fine-tune large language models using RLHF techniques to ensure outputs align with human preferences.
- Apply industry best practices for scaling RLHF workflows to meet the demands of production-grade AI systems.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To arrange a bespoke training session for this course, please contact us to make the necessary arrangements.
Course Outline
Introduction to Reinforcement Learning from Human Feedback (RLHF)
- What is RLHF and its significance.
- Comparison with supervised fine-tuning methods.
- Applications of RLHF in modern AI systems.
Reward Modelling with Human Feedback
- Collecting and structuring human feedback.
- Building and training reward models.
- Evaluating the effectiveness of reward models.
Training with Proximal Policy Optimization (PPO)
- Overview of PPO algorithms for RLHF.
- Implementing PPO with reward models.
- Iteratively and safely fine-tuning models.
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
- A solid understanding of the fundamentals of supervised and reinforcement learning.
- Practical experience with model fine-tuning and neural network architectures.
- Familiarity with 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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