CalibForge generates terminal-agent training tasks by revising candidates until a strong solver passes and a weak solver fails, and students trained on the resulting 5,431 tasks gain up to 30 points on held-out benchmarks.
DeNovoSWE: Scaling Long-Horizon Environments for Generating Entire Repositories from Scratch
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abstract
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 \textbf{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%.
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2026 1verdicts
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CalibForge: Adversarial Solver Calibration for Scaling Learnable Terminal Tasks
CalibForge generates terminal-agent training tasks by revising candidates until a strong solver passes and a weak solver fails, and students trained on the resulting 5,431 tasks gain up to 30 points on held-out benchmarks.