Built on the principles of diversity, deliberative reasoning, and rigorous filtering, STAR-1 integrates and refines data from multiple sources to provide policy-grounded reasoning samples.
The dataset contains 1,000 carefully selected examples, each aligned with best safety practices through GPT-4o-based evaluation.
Fine-tuning with STAR-1 leads to significant safety improvements across multiple benchmarks, with minimal impact on reasoning capabilities.

We open-sourced our STAR1-R1-Distill-32B model here, which is fine-tuned on STAR-1 dataset.

Artifacts

Data

Dataset	Num. of Sample	URL
STAR-1	1K	🤗 UCSC-VLAA/STAR-1
STAR 41K	41K	🤗 UCSC-VLAA/STAR-41K
STAR-benign-915	915	🤗 UCSC-VLAA/STAR-benign-915

Model

Model	Type	URL
`STAR1`-R1-Distill-1.5B	R1-Distill-Qwen-1.5B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-1.5B
`STAR1`-R1-Distill-7B	R1-Distill-Qwen-7B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-7B
`STAR1`-R1-Distill-8B	R1-Distill-Llama-8B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-8B
`STAR1`-R1-Distill-14B	R1-Distill-Qwen-14B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-14B
`STAR1`-R1-Distill-32B	R1-Distill-Qwen-32B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-32B

Evaluation

See our github repo.

Acknowledgement

This work is partially supported by a gift from Open Philanthropy. We thank the NAIRR Pilot Program and the Microsoft Accelerate Foundation Models Research Program for supporting our computing needs.

Citation

@article{wang2025star1saferalignmentreasoning,
    title={STAR-1: Safer Alignment of Reasoning LLMs with 1K Data}, 
    author={Zijun Wang and Haoqin Tu and Yuhan Wang and Juncheng Wu and Jieru Mei and Brian R. Bartoldson and Bhavya Kailkhura and Cihang Xie},
    year={2025},
    journal = {arXiv preprint arXiv:2504.01903}
}

UCSC-VLAA/STAR1-R1-Distill-32B

🌟 STAR-1: Safer Alignment of Reasoning LLMs with 1K Data

Introduction