Do Code Execution Methods Fall Short on Math Reasoning Robustness?

Large Language Models (LLMs) have dazzled many with their impressive accuracy on math reasoning tasks. Yet, when problems undergo minor tweaks, such as changing names or numbers, their performance falters. The promise of code execution methods, which allow these models to generate and execute Python code, instead of relying solely on natural language reasoning, is touted as a potential fix. However, their true impact on improving reasoning robustness remains largely unexamined.

Testing the Waters

A recent study took a rigorous approach to this question. It evaluated three distinct methods using a dataset of 1,000 problems from GSM-Symbolic. The methods included chain-of-thought (CoT) prompting, single-shot code execution via Program-Aided Language models (PAL), and iterative code execution through Step-by-Step Coding (SBSC). Their performance was then compared on original problems and those modified with simple variations using the Claude Haiku 4.5 model.

The data shows that CoT emerged as the frontrunner in robustness. It suffered an accuracy drop of just 1.3 percentage points, with 1.8% of problems breaking under perturbation. PAL proved less reliable, with a 1.7 percentage point drop and 3.1% of problems breaking. SBSC fell somewhere in between these two.

The Numbers Tell the Tale

Here's how the numbers stack up: While the differences weren't statistically significant (p =.096), the trend was clear. Code execution, whether single-shot or iterative, didn't significantly bolster reasoning robustness against simple problem variations. This raises a critical question: Are we overestimating the benefits of code execution methods in enhancing LLM performance on varied mathematical tasks?

The competitive landscape shifted this quarter, with CoT demonstrating that traditional reasoning prompts might hold more resilience than their code-executing counterparts. Comparing these methods against each other, it's apparent that relying on code execution doesn't guarantee improved robustness. It seems the allure of running code doesn't translate into real-world performance gains problem variations.

Why It Matters

Valuation context matters more than the headline number. This finding should prompt a reevaluation of our current approach to enhancing LLMs in mathematical reasoning. If simple modifications can derail performance, the implications for more complex, real-world applications are concerning.

What does this mean for the future of LLMs? While code execution methods sound promising, they might not be the silver bullet we've been hoping for robustness. As researchers and developers, the challenge remains: how do we create models that can handle not only the problem as presented but also its variations? The market map tells the story, and right now, it suggests that the pursuit of robustness continues.