Advancing AI Reasoning: Meta-CoT and System 2 Thinking | by Kaushik Rajan | Jan, 2025

How Meta-CoT enhances system 2 reasoning for complex AI challenges

What makes a language model smart? Is it predicting the next word in a sentence ‒ or handling tough reasoning tasks that challenge even bright humans? Today’s Large Language Models (LLMs) create smooth text plus solve simple problems but they struggle with challenges needing careful thought, like hard math or abstract problem-solving.

This issue comes from how LLMs handle information. Most models use System 1-like thinking ‒ fast, pattern based reactions similar to intuition. While it works for many tasks, it fails when problems need logical reasoning along with trying different approaches and checking results. Enter System 2 thinking ‒ a human method for tackling hard challenges: careful, step-by-step ‒ often needing backtracking to improve conclusions.

To fix this gap, researchers introduced Meta Chain-of-Thought (Meta-CoT). Building on the popular Chain-of-Thought (CoT) method, Meta-CoT lets LLMs model not just steps of reasoning but the whole process of “thinking through a problem.” This change is like how humans tackle tough questions by exploring along with evaluating ‒ and iterating toward answers.