Locating files and functions requiring modification in large software repositories is challenging due to their scale and structural complexity. Existing LLM-based methods typically treat this as a repository-level retrieval task and rely on multiple auxiliary tools, which often 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 base pretrained model, without relying on 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 the 32B model exceeding closed-source models such as GPT-5 on most metrics. 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：在大型软件仓库中定位需要修改的文件和函数具有挑战性，这源于其庞大的规模与复杂的结构。现有基于大语言模型的方法通常将此视为仓库级检索任务，并依赖多种辅助工具，但这些方法往往忽略代码执行逻辑，并使模型控制复杂化。我们提出了RepoNavigator——一种仅配备单一执行感知工具（跳转至被调用符号的定义）的大语言模型智能体。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通过强化学习从基础预训练模型直接进行端到端训练，无需依赖闭源蒸馏。实验表明，经强化学习训练的RepoNavigator实现了最先进的性能：其7B模型超越14B基线模型，14B模型优于32B竞品，32B模型在多数指标上超过了GPT-5等闭源模型。这些结果证实，将单一结构基础工具与强化学习训练相结合，为仓库级问题定位提供了高效且可扩展的解决方案。