Pheromone-based Learning of Optimal Reasoning Paths

• URL: http://arxiv.org/abs/2501.19278v1
• Date: Fri, 31 Jan 2025 16:42:31 GMT
• Title: Pheromone-based Learning of Optimal Reasoning Paths
• Authors: Anirudh Chari, Aditya Tiwari, Richard Lian, Suraj Reddy, Brian Zhou,
• Abstract summary: Ant Colony Optimization-guided Tree of Thought (ACO-ToT)<n>Ant Colony Optimization-guided Tree of Thought (ACO-ToT)<n>Ant Colony Optimization-guided Tree of Thought (ACO-ToT)
• Score: 0.5662299435213421
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities through chain-of-thought prompting, yet discovering effective reasoning methods for complex problems remains challenging due to the vast space of possible intermediate steps. We introduce Ant Colony Optimization-guided Tree of Thought (ACO-ToT), a novel algorithm that combines ACO with LLMs to discover optimal reasoning paths for complex problems efficiently. Drawing inspiration from Hebbian learning in neurological systems, our method employs a collection of distinctly fine-tuned LLM "ants" to traverse and lay pheromone trails through a centralized tree of thought, with each ant's movement governed by a weighted combination of existing pheromone trails and its own specialized expertise. The algorithm evaluates complete reasoning paths using a mixture-of-experts-based scoring function, with pheromones reinforcing productive reasoning paths across iterations. Experiments on three challenging reasoning tasks (GSM8K, ARC-Challenge, and MATH) demonstrate that ACO-ToT performs significantly better than existing chain-of-thought optimization approaches, suggesting that incorporating biologically inspired collective search mechanisms into LLM inference can substantially enhance reasoning capabilities.

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