On Data Engineering for Scaling LLM Terminal Capabilities

This paper explores advanced data engineering strategies crucial for scaling large language models (LLMs) to enhance their "terminal capabilities," i.e., their ability to execute complex commands and interact with external tools. It outlines methodologies for curating diverse, high-quality datasets that enable LLMs to reason, plan, and act effectively in real-world computational environments. This work is critical for the practical deployment of autonomous AI agents and intelligent automation systems.