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

Jiale Zhao, Guoxin Chen, mfzzzzzz, Wayne Xin Zhao, Ruihua Song, Ji-Rong Wen, Kai Jia

DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch: 37 upvotes on Hugging Face Daily Papers, #8 of 42 papers on 2026-06-11. Day-by-day upvote history.

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%.

Paper page on Hugging Face · arXiv

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