license, language, library_name, pipeline_tag, base_model, datasets

license	language	library_name	pipeline_tag	base_model	datasets
mit	en	transformers	text-generation	Qwen/Qwen2.5-Coder-7B-Instruct	TIGER-Lab/SWE-Next-SFT-Trajectories TIGER-Lab/SWE-Next

SWE-Next: Scalable Real-World Software Engineering Tasks for Agents

SWE-Next-7B

SWE-Next-7B is a repository-level software engineering agent fine-tuned from Qwen/Qwen2.5-Coder-7B-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-7B-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. 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-7B 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 7B 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-14B
• Project code: github.com/TIGER-AI-Lab/SWE-Next

Citation

@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},
}