How to Make Smaller LLMs R1-Smart

Overview

Syllabus

• Introduction to Language Models
Overview of Large Language Models (LLMs)
Challenges faced by smaller LLMs
• Understanding R1-Smart Techniques
Origin and purpose of R1-Smart techniques
Key components of R1-Smart for enhancing LLMs
• Post-SFT (Supervised Fine-Tuning) Considerations
Overview of Supervised Fine-Tuning
Limitations and capabilities after SFT
• Enhancing Reasoning Capabilities
Techniques for improving deductive reasoning
Strategies for enhancing inductive reasoning
Addressing common reasoning errors
• Practical Application of R1-Smart Techniques
Step-by-step guide to implementing R1-Smart for smaller LLMs
Case studies showing successful enhancements
• Evaluating Enhanced LLMs
Metrics for assessing reasoning capabilities
Comparing enhanced LLMs to baselines
• Limitations and Future Directions
Current limitations of R1-Smart LLMs
Research frontiers and emerging methodologies
• Hands-on Project
Design and develop a smaller LLM with improved reasoning
Analyze improvements and discuss findings
• Conclusion
Recap of key concepts
Final thoughts on R1-Smart techniques and smaller LLMs

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Subjects

Computer Science