OTC: Optimal Tool Calls via Reinforcement Learning
Authors: name: Hongru Wang, name: Cheng Qian, name: Wanjun Zhong, name: Xiusi Chen, name: Jiahao Qiu, name: Shijue Huang, name: Bowen Jin, name: Mengdi Wang, name: Kam-Fai Wong, name: Heng Ji

Tool-integrated reasoning (TIR) augments large language models (LLMs) with
the ability to invoke external tools, such as search engines and code
interpreters, to solve tasks beyond the capabilities of language-only
reasoning. While reinforcement learning (RL) has shown promise in improving TIR
by optimizing final answer correctness, existing approaches often overlook the
efficiency and cost associated with tool usage. This can lead to suboptimal
behavior, including excessive tool calls that increase computational and
financial overhead, or insufficient tool use that compromises answer quality.
In this work, we propose Optimal Tool Call-controlled Policy Optimization
(OTC-PO), a simple yet effective RL-based framework that encourages models to
produce accurate answers with minimal tool calls. Our method introduces a
tool-integrated reward that jointly considers correctness and tool efficiency,
promoting high tool productivity. We instantiate this framework within both
Proximal Policy Optimization (PPO) and Group Relative Preference Optimization
(GRPO), resulting in OTC-PPO and OTC-GRPO. Experiments with Qwen-2.5 and
Qwen-Math across multiple QA benchmarks show that our approach reduces tool
calls by up to 73.1\% and improves tool productivity by up to 229.4\%, while
maintaining comparable answer accuracy. To the best of our knowledge, this is
the first RL-based framework that explicitly optimizes tool-use efficiency in
TIR.