CodeScout-1.7B

📄 Paper • 💻 Code • 🤗 Collection

Compact yet powerful — outperforms 8× larger Qwen3-14B using only a Unix terminal.

CodeScout-1.7B 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

• Outperforms 8× larger Qwen3-14B with absolute F1 gains of 11–18% for files and 10–15% for functions
• Competitive with 18× larger Qwen3-32B (Thinking), surpassing it by 3–6% in function F1
• Matches RepoNavigator-7B performance while being 4× smaller
• Demonstrates that RL + distillation can compress strong code search into a 1.7B model

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

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-1.7B is trained in two stages:

Stage 1 — Rejection Fine-Tuning (RFT): Qwen3-1.7B is warm-started via supervised fine-tuning on 4K perfect-score trajectories (F1 = 1.0 at all granularities) sampled from CodeScout-14B, yielding the CodeScout-1.7B-RFT checkpoint.

Stage 2 — RL Training: CodeScout-1.7B-RFT is further trained with GSPO reinforcement learning.

• Training data (RL): 800 instances (disjoint from RFT data)
• RL steps: 100
• Batch size: 8, with 8 rollouts per instance
• Max context length: 32K tokens
• Max turns per episode: 4
• 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-1.7B 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

@misc{sutawika2026codescouteffectiverecipereinforcement,
      title={CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents}, 
      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},
      year={2026},
      eprint={2603.17829},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2603.17829}, 
}