Enhancing LLMs’ Reasoning Abilities with Minimal Supervision focuses on optimizing the reasoning capabilities of large language models using a self-reinforcement method that starts with supervised fine-tuning and progresses through iterative improvements. This approach leverages a novel dataset, PuzzleBen, which includes a mix of annotated and unannotated questions to foster model learning.

Key Elements:

• Self-Reinforcement Learning: Begins with a base of supervised fine-tuning and evolves through self-improvement.
• PuzzleBen Dataset: Specially curated dataset with both annotated and unannotated questions for enhanced training.
• Scalable Methodology: Provides a feasible route for LLMs to improve reasoning without extensive data annotations.
• Breadth of Applications: Suitable for various reasoning tasks including puzzles, riddles, and critical reasoning.
• Future Impact: Points towards efficient use of AI in educational and problem-solving applications.

Explore more and access the upcoming dataset here.