On Effectiveness and Efficiency of Agentic Tool-calling and RL Training

• URL: http://arxiv.org/abs/2606.00135v1
• Date: Thu, 28 May 2026 22:21:47 GMT
• Title: On Effectiveness and Efficiency of Agentic Tool-calling and RL Training
• Abstract summary: This paper studies tool-calling along two complementary axes: effectiveness and efficiency.<n>We show that results can be highly sensitive to seemingly minor, often undocumented implementation choices.<n>We introduce two techniques that accelerate RL-based tool-calling training, achieving substantial wall-clock speedup without degrading performance.
• Score: 33.47538664943472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool-calling is a central component of modern large language model (LLM) agents, equipping them with skills beyond their parametric knowledge. This paper studies tool-calling along two complementary axes: effectiveness, i.e., how this capability is measured, and efficiency, i.e., how it is learned. On effectiveness, we systematically analyze tool-calling evaluation pipelines and show that results can be highly sensitive to seemingly minor, often undocumented implementation choices including the random seed, system prompt, multi-turn template construction, and how prior interaction/reasoning history is carried forward. These choices can lead to substantial differences in reported performance, especially in multi-turn settings where without rigorous standardization, leaderboard rankings are unreliable. On efficiency, we examine standard reinforcement learning (RL) for tool-calling and identify two sources of computational waste: (i) during rollouts, many prompts produce no learning signal, and (ii) during policy updates, optimization incurs high computational cost. Guided by these findings, we introduce two techniques that accelerate RL-based tool-calling training, achieving substantial wall-clock speedup without degrading performance.

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