Dissecting Tool-Integrated Reasoning: An Empirical Study and Analysis
This work addresses the need to understand and optimize reasoning in LLMs for complex tasks, though it is incremental as it builds on existing TIR methods with new evaluation tools.
The paper tackled the problem of evaluating how Tool-Integrated Reasoning (TIR) improves the reasoning ability of Large Language Models (LLMs) by introducing the ReasonZoo benchmark and new efficiency metrics, finding that TIR-enabled models consistently outperform non-TIR counterparts and enhance reasoning efficiency with reduced overthinking.
Large Language Models (LLMs) have made significant strides in reasoning tasks through methods like chain-of-thought (CoT) reasoning. However, they often fall short in tasks requiring precise computations. Tool-Integrated Reasoning (TIR) has emerged as a solution by incorporating external tools into the reasoning process. Nevertheless, the generalization of TIR in improving the reasoning ability of LLM is still unclear. Additionally, whether TIR has improved the model's reasoning behavior and helped the model think remains to be studied. We introduce ReasonZoo, a comprehensive benchmark encompassing nine diverse reasoning categories, to evaluate the effectiveness of TIR across various domains. Additionally, we propose two novel metrics, Performance-Aware Cost (PAC) and Area Under the Performance-Cost Curve (AUC-PCC), to assess reasoning efficiency. Our empirical evaluation demonstrates that TIR-enabled models consistently outperform their non-TIR counterparts in both mathematical and non-mathematical tasks. Furthermore, TIR enhances reasoning efficiency, as evidenced by improved PAC and AUC-PCC, indicating reduced overthinking and more streamlined reasoning. These findings underscore the domain-general benefits of TIR and its potential to advance LLM capabilities in complex reasoning tasks.