🤖 AI Summary
This work addresses the significant limitations of large language models (LLMs) in autonomously executing algorithms and performing complex structured reasoning. To overcome these challenges, the authors propose the LLM-DAL framework, which employs a supervised training approach based on Decompositional Algorithmic Learning. This method explicitly guides the model to decompose and internalize algorithmic reasoning steps during training. By doing so, LLM-DAL substantially enhances the model’s capability to execute and generalize on algorithmic tasks—particularly complex arithmetic functions—thereby breaking through inherent bottlenecks in structured reasoning. The framework offers a novel pathway toward improving the systematic reasoning abilities of large language models, demonstrating that explicit decomposition of algorithmic processes can lead to more robust and generalizable performance in tasks requiring precise, stepwise logic.
📝 Abstract
Large Language Models (LLMs) have recently developed new advanced functionalities. Their effectiveness relies on statistical learning and generalization capabilities. However, they face limitations in internalizing the data they process and struggle, for instance, to autonomously execute algorithms. In this paper, we investigate the possibility of extending these models'capabilities to algorithm execution through specialized supervised training focused on reasoning decomposition. We introduce a training model called LLM-DAL (Large Language Model - Decompositional Algorithmic Learning), through which we demonstrate that LLMs'ability to perform complex algorithmic inferences and generalize can be significantly improved when the training method is properly designed to guide the model in its learning process.