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

📰 News

2026-04-07: SWE-Next is now publicly released!

📖 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.

✨ Highlights

Scaled Environment Generation — SWE-Next is an execution-grounded framework that turns real merged-PR commits into self-verifying SWE tasks, and pairs them with high-signal trajectories.
Repo-quarter Profiles - A reusable environment mechanism that amortizes build and storage cost across temporally nearby commits, substantially reducing resource requirements and accelerating large-scale executable SWE data collection.

🛠️ Setup

Prerequisites

Python 3.10+
Docker (for environment execution)
uv package manager

Installation

curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env

git clone https://github.com/TIGER-AI-Lab/SWE-Next.git
cd SWE-Next
uv venv && source .venv/bin/activate
uv sync && uv pip install -e .

🤗 Data & Models

Pre-built artifacts are available on HuggingFace. Download them into data/ before running the pipeline:

Artifact	Description	Download
`packages_python_filtered`	3,900+ Python package list used as pipeline input	`huggingface-cli download TIGER-Lab/packages_python_filtered --repo-type dataset --local-dir data/packages_python_filtered`
`new_commit_better_repos`	Repos with confirmed NEW_COMMIT_BETTER commits	`huggingface-cli download TIGER-Lab/new_commit_better_repos --repo-type dataset --local-dir data/new_commit_better_repos`
`SWE-Next`	Final curated dataset (2,308 instances)	`huggingface-cli download TIGER-Lab/SWE-Next --repo-type dataset --local-dir data/SWE-Next`
`SWE-Next-SFT-Trajectories`	SFT training trajectories	`huggingface-cli download TIGER-Lab/SWE-Next-SFT-Trajectories --repo-type dataset --local-dir data/SWE-Next-SFT-Trajectories`

Pre-trained models:

Model	Download
SWE-Next-7B	`huggingface-cli download TIGER-Lab/SWE-Next-7B --repo-type model --local-dir LlamaFactory/saves/SWE_Next_7B`
SWE-Next-14B	`huggingface-cli download TIGER-Lab/SWE-Next-14B --repo-type model --local-dir LlamaFactory/saves/SWE_Next_14B`

🐳 Environment Generation

SWE-Next extends environment generation to 3,900+ Python packages.

The supported package list is maintained in data/packages_python_filtered/packages_python_filtered.csv and target repositories in data/new_commit_better_repos/new_commit_better_repos.csv.

🚀 Data Pipeline (One-Click)

run_pr_pipeline.zsh automates the full data collection pipeline. It reads data/packages_python_filtered/packages_python_filtered.csv, clones the repos automatically, and processes them end-to-end. If the CSV is not present it falls back to repos already cloned under outputs/upstream_repos/.

Prerequisites: copy .env.template to .env and fill in your credentials:

OPENAI_API_KEY=...        # required for synthetic issue generation
GITHUB_TOKEN=...          # required for fetching PRs
DOCKERHUB_USERNAME=...    # required for pushing Docker images
DOCKERHUB_TOKEN=...
DOCKERHUB_NAMESPACE=...   # your Docker Hub namespace

Option 1 — Dataset only (runs until outputs/all_new_commit_better_pr.jsonl is produced, no trajectories):

PR_GEN_TRAJ=0 zsh run_pr_pipeline.zsh

Option 2 — Dataset + trajectories (continues to run GPT-5-mini on the collected instances):

PR_GEN_TRAJ=1 PR_TRAJ_LLM_NAME=gpt-5-mini zsh run_pr_pipeline.zsh

To process a specific repo only:

PR_GEN_TRAJ=0 zsh run_pr_pipeline.zsh owner/repo

🏋️ Training

Step 1 — Generate SFT Trajectories

Download the SWE-Next dataset first (see Data & Models), then collect trajectories using a frontier LLM:

python src/swenext/agenthub/run/edit.py runagent_multiple \
  --dataset "data/SWE-Next/SWE_Next_dataset.jsonl" \
  --traj_dir "./traj/swe_next_sft" \
  --max_workers 8 \
  --k -1 \
  --llm_name "gpt-5-mini" \
  --use_fn_calling True \
  --temperature 0.2 \
  --max_steps 40 \
  --backend "docker"

Or skip this step and use the pre-collected trajectories from HuggingFace (download SWE-Next-SFT-Trajectories above).

Step 2 — SFT Training

Clone LlamaFactory into the project root first:

git clone https://github.com/hiyouga/LLaMA-Factory.git LlamaFactory

Install LlamaFactory dependencies, then train (run from the project root):

cd LlamaFactory && pip install -e ".[torch,metrics]" && cd ..

# Train 7B agent
llamafactory-cli train train/swe_next_7B.yaml

# Train 14B agent
llamafactory-cli train train/swe_next_14B.yaml

Trained model checkpoints will be saved to LlamaFactory/saves/SWE_Next_7B and LlamaFactory/saves/SWE_Next_14B.

Step 3 — Evaluate on SWE-Bench Verified

Start a vLLM server with the trained model, then run evaluation:

# Start vLLM server (in a separate terminal)
vllm serve LlamaFactory/saves/SWE_Next_7B \
  --served-model-name SWE-Next-7B \
  --port 8000

# Run evaluation on SWE-Bench Verified (8 parallel workers)
export LLM_BASE_URL="http://127.0.0.1:8000/v1"

python src/swenext/agenthub/run/edit.py runagent_multiple \
  --dataset "R2E-Gym/SWE-Bench-Verified" \
  --split "test" \
  --traj_dir "./traj/swe_bench_verified" \
  --max_workers 8 \
  --k -1 \
  --llm_name "openai/SWE-Next-7B" \
  --use_fn_calling False \
  --temperature 1 \
  --max_steps 40 \
  --backend "docker"

Use the official SWE-Bench evaluation harness for final reported scores.

📝 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}, 
}