Risk-aware Alignment for Safer Language Models
📝 Original Paper Info
- Title: Constrained Language Model Policy Optimization via Risk-aware Stepwise Alignment- ArXiv ID: 2512.24263
- Date: 2025-12-30
- Authors: Lijun Zhang, Lin Li, Wei Wei, Yajie Qi, Huizhong Song, Jun Wang, Yaodong Yang, Jiye Liang
📝 Abstract
When fine-tuning pre-trained Language Models (LMs) to exhibit desired behaviors, maintaining control over risk is critical for ensuring both safety and trustworthiness. Most existing safety alignment methods, such as Safe RLHF and SACPO, typically operate under a risk-neutral paradigm that is insufficient to address the risks arising from deviations from the reference policy and offers limited robustness against rare but potentially catastrophic harmful behaviors. To address this limitation, we propose Risk-aware Stepwise Alignment (RSA), a novel alignment method that explicitly incorporates risk awareness into the policy optimization process by leveraging a class of nested risk measures. Specifically, RSA formulates safety alignment as a token-level risk-aware constrained policy optimization problem and solves it through a stepwise alignment procedure that yields token-level policy updates derived from the nested risk measures. This design offers two key benefits: (1) it mitigates risks induced by excessive model shift away from a reference policy, and (2) it explicitly suppresses low-probability yet high-impact harmful behaviors. Moreover, we provide theoretical analysis on policy optimality under mild assumptions. Experimental results demonstrate that our method achieves high levels of helpfulness while ensuring strong safety and significantly suppresses tail risks, namely low-probability yet high-impact unsafe responses.💡 Summary & Analysis
1. **Basic Understanding**: This study illustrates how CNNs are applied to different image recognition tasks. It's like seeing various camera filters process photos differently. 2. **Intermediate Level Understanding**: The research contrasts traditional methods with newer techniques in medical imaging analysis. Think of it as comparing two recipes using the same ingredients—one is simple and quick, while the other is more complex but potentially tastier. 3. **Advanced Understanding**: By analyzing three major approaches to CNN architecture, the study helps us understand how they differ and which is most effective under certain conditions.📄 Full Paper Content (ArXiv Source)
📊 논문 시각자료 (Figures)
