Open-weight coding agents should hold a fundamental advantage over closed-source systems because they can specialize to private codebases, encoding repository-specific information directly in their weights. Yet the cost and complexity of training has kept this advantage theoretical until now. We present Soft-Verified Efficient Repository Agents (SERA), an efficient method for training coding agents that enables the rapid and cheap creation of agents specialized to private codebases. Using Soft Verified Generation (SVG), we generate thousands of trajectories from any code repository, without requiring unit tests. Beyond repository specialization, we apply SVG to a larger corpus of codebases, generating 200,000+ synthetic trajectories. Using only supervised finetuning (SFT), SERA achieves leading results among fully open-source (open data, method, code) models while matching the performance of open-weight models like Devstral-Small-2. Creating SERA models is 26x cheaper than reinforcement learning and 57x cheaper than previous synthetic data methods to reach equivalent performance. We use our dataset to provide detailed analysis of scaling laws, ablations, and confounding factors for training coding agents. Overall, we believe our work will greatly accelerate research on open coding agents and showcase the advantage of open-source models that can adapt to private codebases. We release SERA as the first model in Ai2's Open Coding Agents series, along with all our code, data, and Claude Code integration to support the research community.

翻译：开放权重编程智能体相较于闭源系统应具有根本优势，因为它们能够专精于私有代码库，将仓库特定信息直接编码到权重中。然而，高昂的训练成本与复杂性至今仍使这一优势停留在理论层面。我们提出软验证高效仓库代理（SERA），这是一种高效训练编程智能体的方法，能够快速且廉价地创建专精于私有代码库的智能体。利用软验证生成（SVG）技术，我们从任意代码仓库中生成数千条轨迹，且无需单元测试。除仓库专精外，我们将SVG应用于更大规模的代码库集合，生成超过20万条合成轨迹。仅使用监督微调（SFT），SERA就在完全开源（开放数据、方法、代码）模型中取得了领先结果，同时匹配了如Devstral-Small-2等开放权重模型的性能。创建SERA模型的成本比强化学习低26倍，比以往合成数据方法低57倍，即可达到同等性能。我们利用数据集对编程智能体训练的缩放定律、消融实验及混杂因素进行了详细分析。总体而言，我们相信这项工作将极大推动开放编程智能体的研究，并展示能够适应私有代码库的开源模型优势。我们将SERA作为Ai2开放编程智能体系列的首个模型发布，同时开源所有代码、数据及Claude Code集成，以支持研究社区。