初始化项目，由ModelHub XC社区提供模型
Model: OpenHands/CodeScout-1.7B-RFT Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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base_model: Qwen/Qwen3-1.7B
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pipeline_tag: text-generation
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tags:
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- code-search
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- code-localization
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- reinforcement-learning
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- agent
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- software-engineering
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- GSPO
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- OpenHands
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- SWE-Bench
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datasets:
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- OpenHands/SWE-smith-py-code-search
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- OpenHands/SWE-Gym-code-search
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- OpenHands/CodeScout_Training_Rollouts
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---
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# CodeScout-1.7B-RFT
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[📄 Paper](https://arxiv.org/abs/2603.17829) • [💻 Code](https://github.com/OpenHands/codescout) • [🤗 Collection](https://huggingface.co/collections/OpenHands/codescout-69b9a6adcf21f348f4db937f)
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**Pre-RL checkpoint — rejection fine-tuned on expert trajectories from CodeScout-14B.**
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<p align="center">
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<img src="codescout_overview.png" alt="CodeScout Overview" width="100%">
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</p>
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CodeScout-1.7B-RFT is part of the **CodeScout** family of open-source RL-trained code search agents.
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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.
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## Key Highlights
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- Warm-start checkpoint for [CodeScout-1.7B](https://huggingface.co/OpenHands/CodeScout-1.7B) RL training
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- Distilled from CodeScout-14B expert trajectories with rejection sampling
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- Useful for researchers studying the effect of RFT vs. RL in agent training pipelines
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- Can be used as a base for custom RL experiments on code search
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## Results
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Performance on SWE-Bench code localization (instance-averaged F1 scores):
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| Benchmark | CodeScout-1.7B | CodeScout-4B | CodeScout-14B |
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|---|---|---|---|
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| **SWE-Bench Verified** — File F1 | 55.46 | 68.52 | **68.57** |
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| **SWE-Bench Verified** — Func F1 | 28.22 | 36.78 | **40.32** |
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| **SWE-Bench Pro** — File F1 | 40.96 | 51.77 | **53.63** |
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| **SWE-Bench Pro** — Func F1 | 18.24 | **29.03** | 28.74 |
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| **SWE-Bench Lite** — File F1 | 56.57 | 67.03 | **71.84** |
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| **SWE-Bench Lite** — Func F1 | 27.07 | 39.87 | **44.43** |
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<p align="center">
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<img src="f1_vs_params_file.png" alt="File-level F1 vs Model Size" width="48%">
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<img src="f1_vs_params_function.png" alt="Function-level F1 vs Model Size" width="48%">
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</p>
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<p align="center"><em>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.</em></p>
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## Training
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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.
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- **Teacher model:** [CodeScout-14B](https://huggingface.co/OpenHands/CodeScout-14B)
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- **Source trajectories:** Rollouts from CodeScout-14B on 7,700 training instances
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- **Filtered data:** 4K trajectories with perfect scores (F1 = 1.0 at file, module, and function level)
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- **SFT epochs:** 1
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- **Learning rate:** 5e-5 with cosine scheduler (warmup ratio 0.1)
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- **Batch size:** 8
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- **Optimizer:** AdamW
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- **Framework:** [veRL](https://github.com/volcengine/verl)
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This checkpoint serves as the starting point for RL training of [CodeScout-1.7B](https://huggingface.co/OpenHands/CodeScout-1.7B).
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## How It Works
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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.
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The model is trained with **GSPO** (Group Sequence Policy Optimization) using multi-level F1 rewards at the file, module, and function level.
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## Intended Use
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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.
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## Limitations
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- Trained and evaluated exclusively on **Python** repositories
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- Designed for code *localization*, not code *editing* or issue resolution
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- Performance may vary on repositories significantly different from the training distribution
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- Requires the OpenHands-Bash scaffold for optimal performance
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## Citation
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```bibtex
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@misc{sutawika2026codescouteffectiverecipereinforcement,
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title={CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents},
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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},
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year={2026},
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eprint={2603.17829},
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archivePrefix={arXiv},
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primaryClass={cs.SE},
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url={https://arxiv.org/abs/2603.17829},
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}
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```
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