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

• URL: http://arxiv.org/abs/2508.08882v4
• Date: Sat, 11 Oct 2025 08:24:16 GMT
• Title: Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning
• Authors: Dayu Wang, Jiaye Yang, Weikang Li, Jiahui Liang, Yang Li,
• Abstract summary: We present MSARL, a framework that explicitly decouples reasoning from tool use.<n>In MSARL, a Reasoning Agent decomposes problems and plans tool invocations, while multiple Tool Agents specialize in specific external tools.<n>On mathematical problem solving with code execution, MSARL significantly improves reasoning stability and final-answer accuracy over single-agent baselines.
• Score: 1.974921946982281
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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