SERA: Soft-Verified Efficient Repository Agents

2026-01-28

AI Summary

Existing open-weight code agents struggle to adapt efficiently and cost-effectively to private codebases, limiting their practical utility. This work proposes an efficient training approach based on Soft Verification-guided Generation (SVG) and Supervised Fine-Tuning (SFT): SVG automatically generates thousands of high-quality execution trajectories from a single private codebase, which are then leveraged via SFT to rapidly construct a specialized code agent. To the best of our knowledge, this is the first method to achieve expert-level adaptation to private repositories under fully open-source conditions, attaining state-of-the-art performance while reducing training costs by 26× compared to reinforcement learning and by 57× relative to existing synthetic data approaches.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• repository specialization
• open-source coding agents
• private codebases
• efficient training
• code generation

Innovation

• SERA
• Soft Verified Generation
• repository specialization
• supervised finetuning
• open coding agents