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

Recent progress in multi-agent systems highlights the promise of specialized agents that collaborate through a division of labor. In contrast, most tool-augmented reasoning systems still adopt a single-agent paradigm, where one large model must interleave high-level reasoning with fine-grained tool operations--a process that often leads to cognitive-load interference and unstable outputs. We propose MSARL (Multi-Small-Agent Reinforcement Learning), a novel framework that explicitly decouples reasoning from tool execution and interpretation. In MSARL, a dedicated reasoning agent focuses on strategic problem decomposition and planning, while a specialized tool agent processes long and complex tool outputs, acting as an adaptive condenser to bridge information gaps. This role-specific separation not only reduces cognitive interference but also accelerates the information flow. To enable effective collaboration, we introduce a hierarchical reinforcement learning approach that uses role-specific and collaboration-based rewards, providing granular feedback to the tool agent and a holistic, trajectory-level signal to the reasoning agent. On mathematical problem-solving with code execution, MSARL achieves more stable reasoning and higher final-answer accuracy than strong single-agent baselines.