LiteCoder-4b-Terminal-preview

LiteCoder-4b-Terminal-preview is part of our series of models specialized in terminal-based interactions and stems from our recent efforts to develop capable small and medium-sized code agent models. The model is fine-tuned from Qwen3-4B-Instruct-2507 on the LiteCoder-SFT-Terminal-preview dataset.

Notably, this model achieves competitive results using fewer than 1,000 training samples. By relying entirely on a fully synthetic pipeline—without converting any existing datasets—we were able to secure significant gains on the challenging Terminal Bench, matching the performance of leading open-source models with extreme data efficiency.

Released Artifacts

2025/12/17
LiteCoder-4b-Terminal-preview	Model	https://huggingface.co/Lite-Coder/LiteCoder-4b-Terminal-preview
LiteCoder-SFT-Terminal-preview	Dataset	https://huggingface.co/datasets/Lite-Coder/LiteCoder-SFT-Terminal-preview

Results

Our models achieve competitive results on Terminal Bench, significantly outperforming general-purpose models of similar (and even larger) sizes.

Terminal Bench 1.0 Performance

Model	Agent	Results
LiteCoder-30a3b-Terminal-preview	Terminus 2	18.75%
Qwen3-30B-A3B-Nex-N1	Terminus 2	18.75%
LiteCoder-4b-Terminal-preview	Terminus 2	13.75%
Qwen3-30B-A3B-Instruct	Terminus 2	12.5%
Qwen3-4B-Instruct	Terminus 2	5.0%

Terminal Bench 2.0 Performance

Model	Agent	Results
LiteCoder-30a3b-Terminal-preview	Terminus 2	5.6%
LiteCoder-4b-Terminal-preview	Terminus 2	3.3%
Qwen3-32B	Terminus 2	1.9%
InternLM3-8B-Nex-N1	Terminus 2	0%
Qwen3-8B	Terminus 2	0%

Citation

@misc{LiteCoder Team,
  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={2025},
}

Future Directions

Scaling Environments: Expanding the diversity of Docker environments and teacher models to improve generalization.
Agentic RL: Implementing Reinforcement Learning specifically for multi-turn agentic workflows.