---
license: mit
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-Coder-14B-Instruct
datasets:
- TIGER-Lab/SWE-Next-SFT-Trajectories
- TIGER-Lab/SWE-Next
---
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
# SWE-Next-14B
SWE-Next-14B is a repository-level software engineering agent fine-tuned from **Qwen/Qwen2.5-Coder-14B-Instruct** on the released **SWE-Next SFT Trajectories**. The model is trained with full-parameter supervised fine-tuning on execution-grounded trajectories collected from real merged pull requests and validated repository environments.
## Introduction
SWE-Next introduces reusable **repo-quarter profiles**, which reuse the same environment across nearby commits in time while keeping each task run separate and reproducible. Using only **30 hours** and **639GB** of environment storage, SWE-Next processes **3,971** seed repositories and **102,582** candidate commit pairs mined from real merged PRs to construct a dataset of **2,308** self-verifying instances. SWE-Next improves downstream pass@1 on SWE-Bench Verified and SWE-Bench Lite with fewer or comparable training trajectories, making large-scale executable data collection far more practical and accessible for research.
## Model Overview
This model is trained on **3,693** selected SFT trajectories derived from the SWE-Next collection. The training data emphasizes clean repository-level repair traces and recovery-style debugging trajectories rather than isolated code-completion examples.
Training recipe summary:
- **Base model**: `Qwen/Qwen2.5-Coder-14B-Instruct`
- **Finetuning**: full-parameter SFT
- **Context length**: 32,768
- **Learning rate**: 1e-5
- **Scheduler**: cosine
- **Dataset**: `TIGER-Lab/SWE-Next-SFT-Trajectories`
## Usage
For full usage details, please refer to the official [SWE-Next GitHub repository](https://github.com/TIGER-AI-Lab/SWE-Next). The repository provides the complete setup and evaluation workflow for released models, including:
- environment and dependency installation,
- dataset and trajectory downloads,
- training configurations for the 7B and 14B models,
- vLLM serving commands and repository-level evaluation scripts.
In particular, the GitHub repo contains the exact commands used to serve SWE-Next-14B and evaluate it on SWE-Bench-style tasks under the SWE-Next execution interface.
## Relationship to the SWE-Next Release
This repo contains the released **14B** model checkpoint. Related artifacts are available separately:
- **Base task dataset**: `TIGER-Lab/SWE-Next`
- **SFT trajectories**: `TIGER-Lab/SWE-Next-SFT-Trajectories`
- **Companion model**: `TIGER-Lab/SWE-Next-7B`
- **Project code**: `github.com/TIGER-AI-Lab/SWE-Next`
## Citation
```bibtex
@misc{liang2026swenextscalablerealworldsoftware,
title={SWE-Next: Scalable Real-World Software Engineering Tasks for Agents},
author={Jiarong Liang and Zhiheng Lyu and Zijie Liu and Xiangchao Chen and Ping Nie and Kai Zou and Wenhu Chen},
year={2026},
eprint={2603.20691},
archivePrefix={arXiv},
primaryClass={cs.SE},
url={https://arxiv.org/abs/2603.20691},
}
```
