Direct Decision-making or Deep Thinking? Examining the Necessity of Reasoning in LLM-based Agent Tasks

Introduction

The landscape of artificial intelligence is rapidly evolving, particularly with the advent of Large Language Model (LLM)-based agents. These agents leverage the sophisticated linguistic and cognitive capabilities of LLMs to perform autonomous decision-making and task execution in a variety of environments. While these models demonstrate impressive adaptability and multi-step reasoning , their reliance on extensive reasoning can lead to computational overhead and inefficiencies.

The emergence of Large Reasoning Models (LRMs) further complicates this picture, promising enhanced reasoning capabilities but also intensifying concerns about the suitability of existing agent frameworks. This raises a pivotal question: Do traditional LLM agent paradigms face disruption with the rise of LRMs, and how necessary is deep reasoning for various agent tasks?

Our Contribution: The LaRMA Framework

To address these questions, this paper introduces LaRMA, an exploratory framework designed to systematically investigate the necessity and impact of reasoning in LLM-based agent scenarios. LaRMA allows for a multidimensional analysis by:

1. Task Segmentation: Dividing tasks into categories such as Tool Usage, Plan Design, and Problem Solving to assess varying reasoning demands.
2. Agent Paradigms: Examining reasoning effects within different agent operational modes, like the interactive ReAct and reflective Reflexion paradigms.
3. Evaluation Dimension: Analyzing a spectrum of LLMs and LRMs (e.g., Claude3.5-sonnet, DeepSeek-R1) using metrics for accuracy, efficiency, and computational cost.

Key Research Questions and Findings

Our study, utilizing the LaRMA framework, seeks to answer several critical research questions:

• How do LRMs and LLMs compare in performance across different agent design paradigms (e.g., ReAct, Reflexion)?
• What is the impact of LRMs on agent efficiency and cost across various task types?
• Is the inherent reasoning process of LRMs a universally necessary component for effective agent functionality?
• Can hybrid architectures, combining LLMs for execution and LRMs for reflection, achieve optimal agent performance?

Our key findings indicate that:

• LRMs generally outperform LLMs in reasoning-intensive tasks like Plan Design, especially when leveraging iterative reflection.
• LLMs tend to be more efficient in execution-driven tasks such as Tool Usage.
• Hybrid LLM-LRM configurations show promise, optimizing performance by balancing execution speed with reasoning depth.
• The enhanced reasoning capabilities of LRMs come with trade-offs, including higher computational costs and behavioral challenges like “overthinking” or “fact-ignoring” tendencies.

This research provides a foundational analysis of the balance between deep thinking and potential overthinking in LRMs, aiming to guide the future design of more effective and efficient AI agents.