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Course Outline
Introduction to Reinforcement Learning from Human Feedback (RLHF)
- An overview of RLHF and its significance for government applications
- A comparison with traditional supervised fine-tuning methods
- Applications of RLHF in modern AI systems for government use
Reward Modeling with Human Feedback
- Methods for collecting and structuring human feedback for government projects
- Building and training reward models to align with public sector objectives
- Evaluating the effectiveness of reward models in government contexts
Training with Proximal Policy Optimization (PPO)
- An overview of PPO algorithms tailored for RLHF applications for government
- Implementing PPO with reward models to enhance government AI systems
- Iteratively fine-tuning models to ensure safety and compliance in public sector workflows
Practical Fine-Tuning of Language Models
- Preparing datasets for RLHF workflows in government settings
- Hands-on fine-tuning of a small language model using RLHF techniques for government
- Addressing challenges and implementing mitigation strategies for government applications
Scaling RLHF to Production Systems
- Infrastructure and compute considerations for deploying RLHF in government environments
- Quality assurance processes and continuous feedback loops for government systems
- Best practices for deployment and maintenance of RLHF models in public sector operations
Ethical Considerations and Bias Mitigation
- Addressing ethical risks associated with human feedback in government applications
- Bias detection and correction strategies to ensure fairness and equity in government AI systems
- Ensuring alignment with public sector values and producing safe outputs for government use
Case Studies and Real-World Examples
- Case study: Fine-tuning ChatGPT using RLHF for government communications
- Other successful RLHF deployments in the public sector
- Lessons learned and insights from industry applications relevant to government operations
Summary and Next Steps
Requirements
- An understanding of the fundamentals of supervised and reinforcement learning for government applications
- Experience with model fine-tuning and neural network architectures in public sector contexts
- Familiarity with Python programming and deep learning frameworks (e.g., TensorFlow, PyTorch) for government use
Audience
- Machine learning engineers for government agencies
- AI researchers in the public sector
14 Hours
