Locating the files and functions requiring modification in large open-source software (OSS) repositories is challenging due to their scale and structural complexity. Existing large language model (LLM)-based methods typically treat this as a repository-level retrieval task and rely on multiple auxiliary tools, which overlook code execution logic and complicate model control. We propose RepoNavigator, an LLM agent equipped with a single execution-aware tool-jumping to the definition of an invoked symbol. This unified design reflects the actual flow of code execution while simplifying tool manipulation. RepoNavigator is trained end-to-end via Reinforcement Learning (RL) directly from a pretrained model, without any closed-source distillation. Experiments demonstrate that RL-trained RepoNavigator achieves state-of-the-art performance, with the 7B model outperforming 14B baselines, the 14B model surpassing 32B competitors, and even the 32B model exceeding closed-source models such as Claude-3.7. These results confirm that integrating a single, structurally grounded tool with RL training provides an efficient and scalable solution for repository-level issue localization.

翻译：在大型开源软件（OSS）仓库中，由于其规模庞大且结构复杂，准确定位需要修改的文件与函数极具挑战。现有基于大语言模型（LLM）的方法通常将此任务视为仓库级检索问题，并依赖多种辅助工具，这种做法既忽视了代码执行逻辑，也使得模型控制变得复杂。我们提出了RepoNavigator，一种仅配备单一执行感知工具——跳转至被调用符号定义——的LLM智能体。这一统一设计既反映了代码执行的实际流程，又简化了工具操作。RepoNavigator通过强化学习（RL）从预训练模型开始进行端到端训练，无需任何闭源蒸馏。实验表明，经RL训练的RepoNavigator实现了最先进的性能：其7B模型超越了14B基线模型，14B模型优于32B竞品，甚至32B模型的表现也超过了Claude-3.7等闭源模型。这些结果证实，将单一且结构基础的工具与RL训练相结合，为仓库级问题定位提供了一种高效且可扩展的解决方案。