Nemotron-SFT-SWE-v3

Dataset Description: Nemotron-SFT-SWE-v3 is a software engineering instruction tuning dataset designed to advance the capabilities of LLMs on SWE-Bench style tasks. It includes agentic trajectories collected using a variety of agent harnesses, including the OpenHands, SWE-agent, and mini-SWE-agent frameworks. This dataset is ready for commercial use. Dataset Owner(s): NVIDIA Corporation Dataset Creation Date: Created on: 2026-06-04 Last Modified on: 2026-06-04 License/Terms of Use: This dataset is licensed under Creative Commons Attribution 4.0 International (CC-BY 4.0). Additional Information: Apache 2.0 License; MIT License; BSD-3 License; BSD-2 License. Intended Usage: This dataset is 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. The trajectories can also be used to benchmark and debug agent policies, improve repository-aware reasoning, and study robust, regression-free code editing behaviors in both academic and production environments. Dataset Characterization Data Collection Method** Hybrid: Automated, Synthetic Labeling Method** Hybrid: Automated, Synthetic Dataset Format Modality: Text Format: JSONL Structure: Text + Metadata Dataset Quantification | Subset | Samples | |--------|---------| | Total | 237,970 | Total Data Storage: ~11.7GB Reference(s): Training Software Engineering Agents and Verifiers with SWE-Gym R2E-Gym: Procedural Environments and Hybrid Verifiers for Scaling Open-Weights SWE Agents SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents The OpenHands Software Agent SDK: A Composable and Extensible Foundation for Production Agents Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for Gen