Overview
Explore inference compute as a new frontier for scaling LLMs, examining how coverage scales with sample numbers and how frameworks like Archon can optimize LLM systems without automated verifiers.
Syllabus
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- 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
Taught by
Tags
On-Demand
