The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suffer from a fundamental capability gap: unlike frontier large models, they lack the inference-time strong generalization to handle complicated, unfamiliar codebases.
We identify that the prevailing Task-Centric Learning (TCL) approach, which scales exposure across disparate repositories, fails to address this limitation. In response, we propose Repository-Centric Learning (RCL), a shift that prioritizes vertical, single-repository depth over horizontal, single-task breadth for SLM training.
To accomplish this depth, we design a four-unit Repository-Centric Experience, transforming static codebases into interactive learning signals, to train SWE-Spot-4B, a family of highly compact models built as repo-specialized experts.
Under controlled comparisons with the same teacher and data budget, RCL consistently outperforms TCL across tasks, teachers, model scales, and programming languages.
On seven target repositories, SWE-Spot-4B experts match or exceed existing open-weight models up to $8\times$ larger (e.g., CWM by Meta, Qwen3-Coder-30B) and surpass or match efficiency-focused commercial models (e.g., GPT-4.1-mini, GPT-5-nano) across multiple SWE tasks.
Further analysis reveals that RCL yields higher training and inference efficiency, deep knowledge acquisition, and can scale to repository-cluster experts.
Our work emphasizes that for building efficient intelligence, repository mastery is a distinct, necessary, and practical dimension that complements general coding capability.