CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents
A prerequisite for coding agents to perform tasks on large repositories is code localization - the identification of relevant files, classes, and functions to work on. While repository-level code localization has been performed using embedding-based retrieval approaches such as vector search, recent work has focused on developing agents to localize relevant code either as a standalone precursor to or interleaved with performing actual work. Most prior methods on agentic code search equip the agent with complex, specialized tools, such as repository graphs derived from static analysis. In this paper, we demonstrate that, with an effective reinforcement learning recipe, a coding agent equip...
| Authors | Lintang Sutawika, Aditya Bharat Soni, Bharath Sriraam R R et al. |
|---|---|
| Primary category | cs.SE |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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