AI RESEARCH
Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation
arXiv CS.AI
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ArXi:2605.25835v1 Announce Type: cross This paper examines the specialization of Small Language Models (SLMs) with up to 4B parameters for generating artifacts in domain-specific languages (DSL). Kubernetes manifests are chosen as the target domain. We propose the context-instrumental data distillation method: the source corpus is formed through synthetic generation and, in an extended scheme, through reverse instruction generation from real Kubernetes YAML files, with pairs included in.