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README.md

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+# SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2603.20691"><img alt="Paper" src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white"></a>
+  <a href="https://tiger-ai-lab.github.io/SWE-Next/"><img alt="Project Page" src="https://img.shields.io/badge/Project%20Page-Website-4285F4?style=for-the-badge&logo=googlechrome&logoColor=white"></a>
+  <a href="https://github.com/TIGER-AI-Lab/SWE-Next"><img alt="Code" src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white"></a>
+  <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next-SFT-Trajectories"><img alt="SFT Trajs" src="https://img.shields.io/badge/SFT%20Trajs-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+  <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next"><img alt="Dataset" src="https://img.shields.io/badge/Dataset-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+  <a href="https://huggingface.co/TIGER-Lab/SWE-Next-7B"><img alt="Model 7B" src="https://img.shields.io/badge/Model%207B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+  <a href="https://huggingface.co/TIGER-Lab/SWE-Next-14B"><img alt="Model 14B" src="https://img.shields.io/badge/Model%2014B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a>
+</p>
+
+## 📰 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](https://github.com/astral-sh/uv) package manager
+
+### Installation
+
+```bash
+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`](data/packages_python_filtered/packages_python_filtered.csv) and target repositories in [`data/new_commit_better_repos/new_commit_better_repos.csv`](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):
+```bash
+PR_GEN_TRAJ=0 zsh run_pr_pipeline.zsh
+```
+
+**Option 2 — Dataset + trajectories** (continues to run GPT-5-mini on the collected instances):
+```bash
+PR_GEN_TRAJ=1 PR_TRAJ_LLM_NAME=gpt-5-mini zsh run_pr_pipeline.zsh
+```
+
+To process a specific repo only:
+```bash
+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](#data--models)), then collect trajectories using a frontier LLM:
+
+```bash
+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](https://github.com/hiyouga/LLaMA-Factory) into the project root first:
+
+```bash
+git clone https://github.com/hiyouga/LLaMA-Factory.git LlamaFactory
+```
+
+Install LlamaFactory dependencies, then train (run from the project root):
+
+```bash
+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:
+
+```bash
+# 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](https://github.com/SWE-bench/SWE-bench) for final reported scores.
+
+## 📝 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}, 
+}
+```