Nemotron-SFT-SWE-v2

Dataset Overview

Nemotron-SFT-SWE-v2 is a software engineering instruction tuning dataset designed to advance the capabilities of LLMs on SWE-Bench style tasks. It includes both agentic trajectories collected using the OpenHands framework and an agentless SWE subset for targeted sub-tasks such as code localization, code repair, and test generation. This dataset is ready for commercial use, intended for LLM engineers and research teams building autonomous software engineering agents and code-focused assistants. It is suitable for supervised fine-tuning and distillation of models that must interpret real-world issue statements, plan multi-step tool use, navigate codebases, and implement fixes in a SWE-Bench–style setting.

Dataset Composition

Subsets

The dataset contains two subsets: – Agentic SWE trajectories: Approximately 46k agent trajectories collected using the OpenHands framework. These trajectories were synthesized using Qwen3-Coder-480B-A35B-Instruct and curated for supervised fine-tuning, aiming to improve model performance on SWE-Bench tasks. Issue statements are sourced from SWE-Gym and R2E-Gym-Subset. – Agentless SWE: Supervised fine-tuning data for agentless software engineering sub-tasks in a SWE-Bench style: code localization, code repair, and test generation. Each example includes task-specific outputs such as a ranked file list for localization, patch edits for repair, and reproduction plus unit tests for test generation.

Data Quantification

Data Format

Modality: Text Format: JSONL Structure: Text + Metadata

License

This dataset is licensed under Creative Commons Attribution 4.0 International (CC-BY 4.0). Additional licenses include Apache 2.0, MIT, BSD-3-Clause, BSD-2-Clause.