Computational Thinking Reasoning in Large Language Models

TL;DR

This paper introduces the Computational Thinking Model (CTM), a framework that enhances large language models' reasoning by integrating computational thinking paradigms and live code execution, improving accuracy and interpretability.

Contribution

The paper presents a novel framework that embeds computational thinking into LLMs, enabling problem decomposition, abstraction, and live computation for better reasoning performance.

Findings

CTM outperforms baseline models on code and math benchmarks.

Increases interpretability and generalizability of LLM reasoning.

Enhances problem-solving accuracy through integrated computational workflows.

Abstract

While large language models (LLMs) have demonstrated remarkable reasoning capabilities, they often struggle with complex tasks that require specific thinking paradigms, such as divide-and-conquer and procedural deduction, \etc Previous researches integrate external, reliable tools to alleviate logical inconsistencies and hallucinations in LLMs' problem-solving processes. However, we argue that the root challenge is more profound: LLMs lack the complex thinking paradigms (\ie, computational thinking) during reasoning. In this paper, we propose Computational Thinking Model (CTM), a novel framework that incorporates computational thinking paradigms into LLMs. This framework enables LLMs to reformulate complex problems through decomposition, abstraction, reduction, and simulation, among other techniques. Specifically, live code execution is seamlessly integrated into the reasoning process, allowing CTM to think by computing. CTM directly instills computational thinking objectives into LLMs through tailored reinforcement learning rewards, which encourages problem simplification, modular planning, and iterative verification. We conduct extensive evaluations on multiple code generation and mathematical benchmarks. The results demonstrate that CTM outperforms conventional reasoning models and tool-augmented baselines in terms of accuracy, interpretability, and generalizability. We hope this study offers valuable insights for AI reasoning, where LLMs can transform problems into robust, verifiable, and scalable computational workflows, much like computer scientists do.