# DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch

arXiv: https://arxiv.org/abs/2606.10728 · HTML: https://mlsanity.com/paper/2606.10728/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2026.

## Built on by (most cited first, 8 of 8)

- [CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks](https://mlsanity.com/paper/2608.06352/) (2026)
- [MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis](https://mlsanity.com/paper/2607.27146/) (2026)
- [Agent-Editing World Model: Rethinking World Modeling for LLM Agents](https://mlsanity.com/paper/2609.28416/) (2026)
- [SWE-MILE: Asynchronous Potential-Induced Milestone Credit Assignment for Long-Horizon Software Engineering Agents](https://mlsanity.com/paper/2609.32631/) (2026)
- [Can Agents Design Libraries for Agents?](https://mlsanity.com/paper/2609.36730/) (2026)
- [E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch](https://mlsanity.com/paper/2609.38335/) (2026)
- [What Did the Agent Actually Do? Evidence-Grounded Oversight for Long-Horizon Agents](https://mlsanity.com/paper/2610.06406/) (2026)
- [ProgramGym: Scaling Training Environments for Long-Horizon Program Reproduction Agents](https://mlsanity.com/paper/2610.13395/) (2026)

## Abstract (arXiv)

As the capabilities of LLM-based code agents continue to advance, their expected role is expanding beyond localized bug fixing in existing codebases toward architecting and implementing complete software repositories from high-level specifications. However, training agents for such long-horizon software engineering tasks remains difficult due to the scarcity of large-scale, verifiable whole-repository generation data. In this paper, we introduce DeNovoSWE, a large-scale dataset for whole-repository generation. DeNovoSWE comprises 4,818 high-quality instances, where each instance requires generating a complete repository from documentation. Our dataset is automatically constructed through a carefully designed sandboxed agentic workflow, enabling scalable curation without human annotation. DeNovoSWE is constructed with "divide and conquer" and critic-repair philosophy. To balance data quality and diversity, we further introduce a difficulty-aware trajectory filtering strategy. Fine-tuning Qwen3-30B-A3B on DeNovoSWE substantially improves long-horizon SWE performance, raising its score on the challenging BeyondSWE-Doc2Repo benchmark from 5.8% to 47.2%.