**SERA：软验证高效存储库代理**

SERA: Soft-Verified Efficient Repository Agents

January 28, 2026

作者: Ethan Shen, Danny Tormoen, Saurabh Shah, Ali Farhadi, Tim Dettmers

cs.AI

摘要

开源权重编程智能体相较于闭源系统应具备一项根本优势：它们能够针对私有代码库进行专业化训练，将仓库特定信息直接编码至模型权重中。然而训练成本与复杂性使得这一优势长期停留于理论层面。我们证明该优势现已具备实践可行性。本文提出软验证高效仓库智能体（SERA），一种高效的编程智能体训练方法，能够快速低成本地创建专用于私有代码库的智能体。仅通过监督微调（SFT），SERA就在完全开源（开放数据、方法、代码）模型中取得了最先进的成果，同时达到与Devstral-Small-2等前沿开源权重模型相媲美的性能。创建SERA模型的成本比强化学习低26倍，比先前达到同等性能的合成数据方法低57倍。我们提出的软验证生成（SVG）方法能够从单个代码库生成数千条轨迹。结合成本效益优势，该方法实现了对私有代码库的专业化适配。除仓库专业化外，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。基于该数据集，我们详细分析了编程智能体训练的缩放规律、消融实验及混杂因素。总体而言，我们相信这项工作将极大加速开源编程智能体的研究进程，并彰显可适配私有代码库的开源模型优势。作为Ai2开源编程智能体系列的首个模型，我们同步发布SERA的全部代码、数据及Claude Code集成方案，以支持研究社区发展。

English

Open-weight coding agents should hold a fundamental advantage over closed-source systems: they can be specialized to private codebases, encoding repository-specific information directly in their weights. Yet the cost and complexity of training has kept this advantage theoretical. We show it is now practical. 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 only supervised finetuning (SFT), SERA achieves state-of-the-art results among fully open-source (open data, method, code) models while matching the performance of frontier 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. Our method, Soft Verified Generation (SVG), generates thousands of trajectories from a single code repository. Combined with cost-efficiency, this enables specialization to private codebases. Beyond repository specialization, we apply SVG to a larger corpus of codebases, generating over 200,000 synthetic trajectories. We use this 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 specialize 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.