CodeScout-1.7B-RFT

📄 Paper • 💻 Code • 🤗 Collection

Pre-RL checkpoint — rejection fine-tuned on expert trajectories from CodeScout-14B.

CodeScout-1.7B-RFT 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

• Warm-start checkpoint for CodeScout-1.7B RL training
• Distilled from CodeScout-14B expert trajectories with rejection sampling
• Useful for researchers studying the effect of RFT vs. RL in agent training pipelines
• Can be used as a base for custom RL experiments on code search

Results

Performance on SWE-Bench code localization (instance-averaged F1 scores):

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-RFT is the intermediate checkpoint produced by rejection fine-tuning (RFT) Qwen3-1.7B on expert trajectories from CodeScout-14B, before the final RL stage.

• Teacher model: CodeScout-14B
• Source trajectories: Rollouts from CodeScout-14B on 7,700 training instances
• Filtered data: 4K trajectories with perfect scores (F1 = 1.0 at file, module, and function level)
• SFT epochs: 1
• Learning rate: 5e-5 with cosine scheduler (warmup ratio 0.1)
• Batch size: 8
• Optimizer: AdamW
• Framework: veRL

This checkpoint serves as the starting point for RL training of CodeScout-1.7B.

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-RFT 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}, 
}