Best efficiency–performance trade-off — outperforms 8× larger Qwen3-32B across all benchmarks.
CodeScout Overview
CodeScout-4B is part of the CodeScout family of open-source RL-trained code search agents.
CodeScout models achieve state-of-the-art repository-level code localization using nothing more than a standard Unix terminal — no static analysis, no repository graphs, no language-specific tooling.
Key Highlights
Consistently outperforms 8× larger Qwen3-32B on all benchmarks
Surpasses RepoNavigator-14B by 2–10% in file F1 and 8–11% in function F1
Exceeds GPT-5 with RepoNavigator by 9% in file F1 and 5% in function F1 on SWE-Bench Verified
Best efficiency–performance trade-off in the CodeScout family
Results
Performance on SWE-Bench code localization (instance-averaged F1 scores):
Benchmark
CodeScout-1.7B
CodeScout-4B
CodeScout-14B
SWE-Bench Verified — File F1
55.46
68.52
68.57
SWE-Bench Verified — Func F1
28.22
36.78
40.32
SWE-Bench Pro — File F1
40.96
51.77
53.63
SWE-Bench Pro — Func F1
18.24
29.03
28.74
SWE-Bench Lite — File F1
56.57
67.03
71.84
SWE-Bench Lite — Func F1
27.07
39.87
44.43
File-level F1 vs Model Size
Function-level F1 vs Model Size
Code localization performance on SWE-Bench Verified. CodeScout (⭐) achieves superior or competitive results over larger open-source LLMs and narrows the gap with closed-source frontier models.
Training
CodeScout-4B is trained directly from Qwen3-4B-Instruct-2507 using GSPO reinforcement learning.
Training data: 1,600 instances from SWE-Smith (39K filtered, 128 repos)
RL steps: 200
Batch size: 8, with 8 rollouts per instance
Max context length: 40K tokens
Max turns per episode: 6
Reward: Multi-level F1 (file + module + function)
Hardware: 8×H100 GPUs
Learning rate: 1e-6 (constant)
How It Works
CodeScout uses the OpenHands-Bash scaffold — an agent equipped with only a Terminal tool (supporting standard Unix commands like rg, find, grep, ls) and a LocalizationFinish tool for structured output submission. The agent iteratively navigates the repository to identify relevant files, classes, and functions related to a given issue.
The model is trained with GSPO (Group Sequence Policy Optimization) using multi-level F1 rewards at the file, module, and function level.
Intended Use
CodeScout-4B is designed for repository-level code localization: given a GitHub issue description and a code repository, it identifies the relevant files, classes, and functions that need to be modified. It is intended to be used as a localization subagent within larger coding agent pipelines.
Limitations
Trained and evaluated exclusively on Python repositories
Designed for code localization, not code editing or issue resolution
Performance may vary on repositories significantly different from the training distribution
Requires the OpenHands-Bash scaffold for optimal performance
Citation
bibtex
1@misc{sutawika2026codescouteffectiverecipereinforcement,
2 title={CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents},
3 author={Lintang Sutawika and Aditya Bharat Soni and Bharath Sriraam R R and Apurva Gandhi and Taha Yassine and Sanidhya Vijayvargiya and Yuchen Li and Xuhui Zhou and Yilin Zhang and Leander Melroy Maben and Graham Neubig},
4 year={2026},
5 eprint={2603.17829},
6 archivePrefix={arXiv},
7 primaryClass={cs.SE},
8 url={https://arxiv.org/abs/2603.17829},
9}
