Announcing LiteCoder-Terminal: Lightweight Terminal Agents with <1k Synthesized Trajectories

LiteCoder-Terminal-Preview, a series of models designed for terminal-based interactions, has been launched, demonstrating competitive performance with less than 1,000 synthesized training samples. Utilizing a fully synthetic data pipeline, the models are able to match leading open-source models in their weight class with high data efficiency. The development involved a three-stage process of task curation, environment preparation, and trajectory generation, focusing on domains like AI/ML, data science, and system administration. The models excel in Terminal Bench tests, outperforming larger general-purpose models, thanks to effective environment adaptability and context maintenance. However, they exhibit sensitivity to agent frameworks, underscoring the need for framework-agnostic training data. Future plans include expanding Docker environments and implementing reinforcement learning for multi-turn workflows.