EMTIR-GRPO: Efficient Multi-Tool Augmented Large Language Models via Reinforcement Learning

Tool-integrated reasoning (TIR) enables large language models (LLMs) to invoke external tools for tasks beyond their internal capacity but often suffers from tool overuse. Existing approaches leverage imitation learning or reward shaping to improve efficiency, yet they mainly target single-tool scenarios and ignore the varying invocation costs across tools in multi-tool reasoning (MTIR). To address these gaps, we propose EMTIR-GRPO, a simple yet effective RL algorithm for cost-aware MTIR. Built upon GRPO, we introduce a composite reward considering format completeness, answer correctness, and tool efficiency. By incorporating a cost-aware coefficient with group optimal cost estimation, EMTIR-GRPO explicitly models heterogeneous tool costs and encourages more cost-effective tool-use strategies. Experiments on MTIR-QA and MTIR-TC demonstrate significant efficiency gains (e.g., ∆+10.9 on Tool-Star-7B and ∆+3.6 on ReCall-7B) while maintaining or even improving accuracy (e.g., 55.4 vs. 52.0 on Tool-Star-7B). Additional budgetconstrained and tool-free evaluations further validate its effectiveness in maximizing costefficiency and reducing cognitive offloading.