Inference Scaling: A New Frontier for AI Capability

Overview

Syllabus

• Introduction to Inference Scaling
Overview of LLMs (Large Language Models)
Significance of Inference Compute in AI
• Understanding Coverage and Sample Numbers
Definition and importance of coverage in AI
How scaling sample numbers improves inference
Case studies/examples
• Frameworks for Optimizing LLM Systems
Introduction to Archon
Techniques for optimizing LLMs without automated verifiers
Implementing and adapting frameworks in existing systems
• Challenges in Inference Scaling
Limitations and potential pitfalls
Addressing scalability and efficiency issues
• Future Directions in Inference Scaling
Emerging technologies and methodologies
Impact on AI capabilities and applications
• Practical Applications and Case Studies
Real-world examples of scalable inference in AI
Discussing successful implementations and lessons learned
• Conclusion and Future Outlook
Summarizing key insights from the course
Exploring the broader impact of inference scaling on AI development

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Subjects

Computer Science