README.md

---
library_name: transformers
license: apache-2.0
language:
- en
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
tags:
- code-search
- code-localization
- reinforcement-learning
- agent
- software-engineering
- GSPO
- OpenHands
- SWE-Bench
datasets:
- OpenHands/SWE-smith-py-code-search
- OpenHands/SWE-Gym-code-search
- OpenHands/CodeScout_Training_Rollouts
---

# CodeScout-1.7B-RFT

[📄 Paper](https://arxiv.org/abs/2507.02875) • [💻 Code](https://github.com/OpenHands/codescout) • [🤗 Collection](https://huggingface.co/collections/OpenHands/codescout-69b9a6adcf21f348f4db937f)

**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](https://huggingface.co/OpenHands/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):


| 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** |


## 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](https://huggingface.co/OpenHands/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](https://github.com/volcengine/verl)

This checkpoint serves as the starting point for RL training of [CodeScout-1.7B](https://huggingface.co/OpenHands/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


```bibtex
@article{sutawika2025codescout,
  title={CodeScout: An Effective Recipe for Reinforcement Learning of Code Search Agents},
  author={Sutawika, Lintang and Soni, Aditya Bharat and R R, Bharath Sriraam and Gandhi, Apurva and Yassine, Taha and Vijayvargiya, Sanidhya and Li, Yuchen and Zhou, Xuhui and Zhang, Yilin and Maben, Leander Melroy and Neubig, Graham},
  journal={arXiv preprint arXiv:2507.02875},
  year={2025}
}
```