DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch
Summary
DeNovoSWE is a large-scale dataset for training code agents to generate entire software repositories from documentation, using a sandboxed agentic workflow and difficulty-aware filtering. Fine-tuning Qwen3-30B-A3B on it boosts performance on the BeyondSWE-Doc2Repo benchmark from 5.8% to 47.2%.
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Paper page - DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch
Source: https://huggingface.co/papers/2606.10728
Abstract
A large-scale dataset called DeNovoSWE is introduced for training code agents to generate entire software repositories from documentation, significantly improving performance on long-horizon software engineering tasks.
As the capabilities ofLLM-based code agentscontinue 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, verifiablewhole-repository generationdata. In this paper, we introduce DeNovoSWE, alarge-scale datasetforwhole-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 designedsandboxed agentic workflow, enabling scalable curation without human annotation. DeNovoSWE is constructed with “divide and conquer” andcritic-repair philosophy. To balance data quality and diversity, we further introduce adifficulty-aware trajectory filteringstrategy.Fine-tuningQwen3-30B-A3Bon DeNovoSWE substantially improves long-horizon SWE performance, raising its score on the challengingBeyondSWE-Doc2Repo benchmarkfrom 5.8% to 47.2%.
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