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

This paper studies tool-calling in large language model (LLM) agents, examining its effectiveness and efficiency. It analyzes evaluation pipelines, showing results are sensitive to implementation choices, and identifies computational waste in reinforcement learning training.