OpenHands/CodeScout-14B
OpenHands/CodeScout-14B is a 14 billion parameter model, part of the CodeScout family of RL-trained code search agents, developed by OpenHands. It achieves state-of-the-art open-source performance on SWE-Bench for repository-level code localization, identifying relevant files, classes, and functions using only standard Unix terminal commands. This model is specifically optimized for Python repositories and excels at pinpointing code modifications within larger coding agent pipelines.
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CodeScout-14B: State-of-the-Art Code Localization
CodeScout-14B, developed by OpenHands, is the strongest model in the CodeScout family, an open-source series of reinforcement learning (RL)-trained code search agents. This 14 billion parameter model is specifically designed for repository-level code localization, identifying relevant files, classes, and functions within a codebase based on a GitHub issue description.
Key Capabilities & Differentiators
- Open-source SOTA Performance: Achieves state-of-the-art results on SWE-Bench Verified, Pro, and Lite for code localization, outperforming models 2–18 times larger.
- Terminal-Based Operation: Operates using only a standard Unix terminal and commands (
rg,find,grep,ls), without relying on static analysis or language-specific tooling. - RL-Trained: Fine-tuned from
Qwen3-14Busing Group Sequence Policy Optimization (GSPO) with multi-level F1 rewards. - Python-Focused: Trained and evaluated exclusively on Python repositories.
Intended Use Cases
- Code Localization: Ideal for pinpointing where code modifications are needed within a repository.
- Subagent in Coding Pipelines: Designed to function as a localization subagent within broader automated coding workflows.
Limitations
- Exclusively trained and evaluated on Python repositories.
- Focuses solely on code localization, not code editing or full issue resolution.
- Requires the OpenHands-Bash scaffold for optimal performance.