On Data Engineering for Scaling LLM Terminal Capabilities

Summary

This paper focuses on how to best train large language models to effectively use a computer's command line, also known as a 'terminal'. It addresses the fact that the methods used to create the training data for these models are usually kept secret.

What's the problem?

Currently, it's difficult for researchers to improve 'terminal agents' – AI that can interact with computers through text commands – because the data used to train the best performing agents isn't publicly available. This makes it hard to understand what makes these agents work well and to build even better ones. Essentially, we don't know *how* the best agents are learning to use the terminal.

What's the solution?

The researchers created a system called Terminal-Task-Gen to automatically generate a large amount of training data for terminal agents. This system can create tasks based on specific starting points ('seed-based') or desired skills ('skill-based'). They then used this system to build a large, publicly available dataset called Terminal-Corpus. Finally, they trained a new family of models, called Nemotron-Terminal, using this dataset and showed they perform very well on standard tests, even compared to much larger models.

Why it matters?

This work is important because it opens up research in this area. By releasing both the training data and the models, other researchers can now build upon this work and develop even more capable AI agents that can automate tasks and interact with computers in a more intelligent way. It democratizes access to the technology and accelerates progress in the field.