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The paper introduces Soft-Verified Efficient Repository Agents (SERA), a supervised finetuning method for efficiently training coding agents specialized to private codebases. SERA leverages Soft Verified Generation (SVG) to create thousands of synthetic trajectories from a single repository, enabling rapid and cost-effective specialization. The resulting SERA models achieve state-of-the-art performance among fully open-source models, matching the performance of models like Devstral-Small-2 at a fraction of the cost compared to reinforcement learning or previous synthetic data methods.
Open-weight coding agents can now be cheaply and rapidly specialized to private codebases, thanks to a new supervised finetuning method that slashes training costs by over 25x.
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.
AI2
UW