English

Towards a Small Language Model Lifecycle Framework

Software Engineering 2025-06-10 v1 Machine Learning

Abstract

Background: The growing demand for efficient and deployable language models has led to increased interest in Small Language Models (SLMs). However, existing research remains fragmented, lacking a unified lifecycle perspective. Objective: This study aims to define a comprehensive lifecycle framework for SLMs by synthesizing insights from academic literature and practitioner sources. Method: We conducted a comprehensive survey of 36 works, analyzing and categorizing lifecycle-relevant techniques. Results: We propose a modular lifecycle model structured into main, optional, and cross-cutting components. The model captures key interconnections across stages, supporting method reuse, co-adaptation, and lifecycle-awareness. Conclusion: Our framework provides a coherent foundation for developing and maintaining SLMs, bridging theory and practice, and guiding future research and tool development.

Keywords

Cite

@article{arxiv.2506.07695,
  title  = {Towards a Small Language Model Lifecycle Framework},
  author = {Parsa Miraghaei and Sergio Moreschini and Antti Kolehmainen and David Hästbacka},
  journal= {arXiv preprint arXiv:2506.07695},
  year   = {2025}
}
R2 v1 2026-07-01T03:06:54.594Z