Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning

Abstract:Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-integrated reasoning systems, however, often follow a single-agent paradigm in which one large model interleaves long-horizon reasoning with precise tool operations, leading to cognitive-load interference and unstable coordination. We present MSARL, a Multi-Small-Agent Reinforcement Learning framework that explicitly decouples reasoning from tool use. In MSARL, a Reasoning Agent decomposes problems and plans tool invocations, while multiple Tool Agents specialize in specific external tools, each trained via a combination of imitation learning and reinforcement learning with role-specific rewards. On mathematical problem solving with code execution, MSARL significantly improves reasoning stability and final-answer accuracy over single-agent baselines. Moreover, the architecture generalizes to diverse tool-use tasks, demonstrating that cognitive-role decoupling with small agents is a scalable blueprint for multi-agent AI design.

Artificial Intelligence (cs.AI)

arXiv:2508.08882 [cs.AI]

(or arXiv:2508.08882v5 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2508.08882 Focus to learn more arXiv-issued DOI via DataCite

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