Exploring the Necessity of Reasoning in LLM-based Agent Scenarios
This research addresses the problem of balancing reasoning and execution in AI agents for developers and researchers, though it is incremental as it builds on existing frameworks.
The study investigated the necessity of reasoning in LLM-based agent scenarios by comparing LLMs and LRMs across tasks, finding that LRMs excel in reasoning-intensive tasks like Plan Design while LLMs are better in execution-driven tasks like Tool Usage, with hybrid configurations optimizing performance by blending execution speed and reasoning depth.
The rise of Large Reasoning Models (LRMs) signifies a paradigm shift toward advanced computational reasoning. Yet, this progress disrupts traditional agent frameworks, traditionally anchored by execution-oriented Large Language Models (LLMs). To explore this transformation, we propose the LaRMA framework, encompassing nine tasks across Tool Usage, Plan Design, and Problem Solving, assessed with three top LLMs (e.g., Claude3.5-sonnet) and five leading LRMs (e.g., DeepSeek-R1). Our findings address four research questions: LRMs surpass LLMs in reasoning-intensive tasks like Plan Design, leveraging iterative reflection for superior outcomes; LLMs excel in execution-driven tasks such as Tool Usage, prioritizing efficiency; hybrid LLM-LRM configurations, pairing LLMs as actors with LRMs as reflectors, optimize agent performance by blending execution speed with reasoning depth; and LRMs' enhanced reasoning incurs higher computational costs, prolonged processing, and behavioral challenges, including overthinking and fact-ignoring tendencies. This study fosters deeper inquiry into LRMs' balance of deep thinking and overthinking, laying a critical foundation for future agent design advancements.