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Starts 6 June 2025 06:58
Ends 6 June 2025
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Overview
Explore Qwen's new WorldPM model that encodes human preferences at scale, solving key RLHF challenges by creating a world model that better aligns AI with human values.
Syllabus
- Introduction to RLHF and World Models
- Understanding Qwen's WorldPM Model
- Encoding Human Preferences at Scale
- Solving Key RLHF Challenges with WorldPM
- Aligning AI with Human Values
- Practical Applications of the WorldPM Model
- Future Directions in World Model Research
- Conclusion and Open Questions
- Project and Assessment
- Additional Resources
Overview of Reinforcement Learning from Human Feedback (RLHF)
Importance of aligning AI with human values
Introduction to world models in AI
Key features of the WorldPM model
Innovations introduced by Qwen in encoding human preferences
Comparison with existing RLHF models
Methodologies for gathering and encoding human preferences
Data scalability and its impact on model performance
Ethical considerations in collecting and using human preference data
Identifying and addressing common RLHF alignment issues
Role of the WorldPM model in resolving these challenges
Case studies of Qwen's model in real-world applications
Techniques for integrating human values in AI systems
Discussion of value alignment metrics
Potential pitfalls and considerations in value alignment
Industry examples: healthcare, financial services, and more
Predicting societal impacts and future trends
Emerging trends in world model development
Sustainability and long-term effectiveness of value-aligned AI
Recap of key learning points
Open research questions and areas for further exploration
Overview of the course project on implementing WorldPM
Evaluation criteria and assessment methods
Suggested readings and resources for deeper exploration
List of influential papers and current research in the field
Subjects
Computer Science