Audit your tool-calling RL setup for rollout compute waste before scaling training

arXiv:2606.00135·medium signal

'On Effectiveness and Efficiency of Agentic Tool-calling and RL Training' (arXiv:2606.00135) runs the first systematic analysis of where standard RL for tool-calling burns compute during rollouts, and finds that seemingly minor design choices cause substantial swings in benchmark reliability. The practical lesson: tool-calling RL benchmarks are fragile, so profile your rollout pipeline and pin design decisions before you attribute gains to the algorithm. Treat efficiency-of-rollout as a first-class knob, not an afterthought.