LiteCoder-Terminal-4b-sft is part of our latest release on lightweight code agents. The model is fine-tuned from Qwen3-4B-Instruct-2507 on the LiteCoder-Terminal-SFT dataset.
Compared to our previous preview version, we scaled up the training data from under 1,000 samples to 11,255 trajectories, incorporating a broader task taxonomy and diverse agent scaffolds. With these updates, the model shows consistent improvements across Terminal Bench evaluations.
@misc{litecoder2026,
title={LiteCoder: Advancing Small and Medium-sized Code Agents},
author={Xiaoxuan Peng and Xinyu Lu and Kaiqi Zhang and Taosong Fang and Boxi Cao and Yaojie Lu},
year={2026},
}