CodeScout-4B: An Efficient Code Localization Agent

CodeScout-4B is a 4 billion parameter model developed by OpenHands, part of the CodeScout family of open-source, RL-trained code search agents. It is specifically designed for repository-level code localization, identifying relevant files, classes, and functions within a codebase based on a given GitHub issue description.

Key Capabilities & Performance

• Superior Efficiency: Consistently outperforms 8x larger models like Qwen3-32B across all benchmarks.
• High Accuracy: Surpasses RepoNavigator-14B by 2-10% in file F1 and 8-11% in function F1. It also exceeds GPT-5 with RepoNavigator by 9% in file F1 and 5% in function F1 on SWE-Bench Verified.
• Terminal-Based Operation: Achieves state-of-the-art results using only a standard Unix terminal and basic commands (e.g., rg, find, grep, ls), without requiring static analysis or language-specific tooling.
• Reinforcement Learning: Trained directly from Qwen3-4B-Instruct-2507 using GSPO (Group Sequence Policy Optimization) with multi-level F1 rewards on the SWE-Smith dataset.

Intended Use & Limitations

CodeScout-4B is ideal for integration as a localization subagent within larger coding agent pipelines. It is specifically trained and evaluated on Python repositories for code localization, not code editing or issue resolution. Optimal performance requires the OpenHands-Bash scaffold.