This paper introduces STAR-1, a high-quality, just-1k-scale safety dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1. Built on three core principles -- diversity, deliberative reasoning, and rigorous filtering -- STAR-1 aims to address the critical needs for safety alignment in LRMs. Specifically, we begin by integrating existing open-source safety datasets from diverse sources. Then, we curate safety policies to generate policy-grounded deliberative reasoning samples. Lastly, we apply a GPT-4o-based safety scoring system to select training examples aligned with best practices. Experimental results show that fine-tuning LRMs with STAR-1 leads to an average 40% improvement in safety performance across four benchmarks, while only incurring a marginal decrease (e.g., an average of 1.1%) in reasoning ability measured across five reasoning tasks. Extensive ablation studies further validate the importance of our design principles in constructing STAR-1 and analyze its efficacy across both LRMs and traditional LLMs. Our project page is https://ucsc-vlaa.github.io/STAR-1.

本文提出了STAR-1，一个专为大型推理模型（LRMs）设计的高质量、安全数据集，仅规模为1K。该研究通过整合多样的开源安全数据集，制定安全政策并生成相应的推理样本，从而显著提高了LRMs的安全对齐性能，实验证明在四个基准测试中安全性能平均提升了40%，而推理能力仅平均下降1.1%。

STAR-1：基于1K数据的更安全推理大型模型对齐