CodeSpecWeave builds coding tasks with interdependent specifications
Original title: When Requirements Interact: Scaling Long-Horizon Coding via Interdependent Specifications
The pipeline produces 4,670 tasks across 2,865 repositories and a 100-task benchmark with 1,044 specifications.
CodeSpecWeave synthesizes long-horizon coding tasks whose requirements interact, such as interface compatibility and error handling, rather than checklist-style goals. A synthesis agent writes coupled requirements and executable tests from real developer seeds, while a review agent audits solution trajectories from multiple models. The authors say reinforcement learning on these tasks improves a base model on DeepSWE-v1.1 and largely beats existing baselines. CodeSpecWeave-Bench still shows headroom for frontier models on interdependent constraints.
Unconfirmed: Performance gains are reported by the paper’s authors and are not independently verified here.
- The training set has 4,670 tasks from 2,865 repositories.
- CodeSpecWeave-Bench contains 100 tasks and 1,044 specifications.
- Authors report the RL model largely outperforms existing DeepSWE-v1.1 baselines.
- A review agent checks trajectories from multiple models for test reliability.
From the original article
1 min read at huggingface.co
To succeed in real-world long-horizon software development, coding agents must satisfy multi-dimensional task specifications, such as interface compatibility and error handling, and do so without breaking existing dependencies. However, current works lack specification in both quantity and diversity, often emphasizing primary goals while ignoring complex constraints.
By Yishuo Cai, Minzheng Wang, Liang Zhao · huggingface.co
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