Oyster-II: Reinforcement Learning for Constructive Safety Alignment in Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities across diverse applications, yet ensuring their simultaneous safety, helpfulness, and trustworthiness remains a persistent challenge. Conventional refusal-oriented alignment strategies mitigate harmful content generation but systematically fail to serve legitimate user needs, often withholding information that could safely and constructively address the underlying intent of sensitive queries. Building upon the constructive safety paradigm pioneered by Oyster-I, which moves beyond blanket refusal toward thoughtful, response-oriented safety alignment, we identify two critical limitations of its Supervised Fine-Tuning (SF...
| Authors | Jiyang Guan, Yong Xie, Jun Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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