What You Need to Know Before
You Start
Starts 8 June 2025 00:29
Ends 8 June 2025
00
days
00
hours
00
minutes
00
seconds
31 minutes
Optional upgrade avallable
Not Specified
Progress at your own speed
Free Video
Optional upgrade avallable
Overview
Discover how to enhance smaller language models with R1-Smart techniques from UC Berkeley researchers, exploring reasoning capabilities and limitations after SFT.
Syllabus
- Introduction to Language Models
- Understanding R1-Smart Techniques
- Post-SFT (Supervised Fine-Tuning) Considerations
- Enhancing Reasoning Capabilities
- Practical Application of R1-Smart Techniques
- Evaluating Enhanced LLMs
- Limitations and Future Directions
- Hands-on Project
- Conclusion
Overview of Large Language Models (LLMs)
Challenges faced by smaller LLMs
Origin and purpose of R1-Smart techniques
Key components of R1-Smart for enhancing LLMs
Overview of Supervised Fine-Tuning
Limitations and capabilities after SFT
Techniques for improving deductive reasoning
Strategies for enhancing inductive reasoning
Addressing common reasoning errors
Step-by-step guide to implementing R1-Smart for smaller LLMs
Case studies showing successful enhancements
Metrics for assessing reasoning capabilities
Comparing enhanced LLMs to baselines
Current limitations of R1-Smart LLMs
Research frontiers and emerging methodologies
Design and develop a smaller LLM with improved reasoning
Analyze improvements and discuss findings
Recap of key concepts
Final thoughts on R1-Smart techniques and smaller LLMs
Subjects
Computer Science
