SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Peng, Jinjun Saebo, Magnus Zhong, Tianjun Cheng, Yi-Jie Yang, Junfeng Ray, Baishakhi Chen, Simin Ding, Yangruibo |
| author_facet | Peng, Jinjun Saebo, Magnus Zhong, Tianjun Cheng, Yi-Jie Yang, Junfeng Ray, Baishakhi Chen, Simin Ding, Yangruibo |
| contents | 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 work with complicated, unfamiliar codebases. We identify that the prevailing Task-Centric Learning (TCL) paradigm, which scales exposure across disparate repositories, fails to address this limitation. In response, we propose Repository-Centric Learning (RCL), a paradigm shift that prioritizes vertical repository depth over horizontal task breadth, suggesting SLMs must internalize the "physics" of a target software environment through parametric knowledge acquisition, rather than attempting to recover it via costly inference-time search. Following this new paradigm, 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 that breaks established scaling trends, outperforming open-weight models up to larger (e.g., CWM by Meta, Qwen3-Coder-30B) and surpassing/matching 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 sample efficiency and lower inference costs, emphasizing that for building efficient intelligence, repository mastery is a distinct and necessary dimension that complements general coding capability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21649 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning Peng, Jinjun Saebo, Magnus Zhong, Tianjun Cheng, Yi-Jie Yang, Junfeng Ray, Baishakhi Chen, Simin Ding, Yangruibo Machine Learning Artificial Intelligence Computation and Language Software Engineering 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 work with complicated, unfamiliar codebases. We identify that the prevailing Task-Centric Learning (TCL) paradigm, which scales exposure across disparate repositories, fails to address this limitation. In response, we propose Repository-Centric Learning (RCL), a paradigm shift that prioritizes vertical repository depth over horizontal task breadth, suggesting SLMs must internalize the "physics" of a target software environment through parametric knowledge acquisition, rather than attempting to recover it via costly inference-time search. Following this new paradigm, 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 that breaks established scaling trends, outperforming open-weight models up to larger (e.g., CWM by Meta, Qwen3-Coder-30B) and surpassing/matching 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 sample efficiency and lower inference costs, emphasizing that for building efficient intelligence, repository mastery is a distinct and necessary dimension that complements general coding capability. |
| title | SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning |
| topic | Machine Learning Artificial Intelligence Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2601.21649 |