@inproceedings{li-etal-2026-gspr,
title = "{GSPR}: Aligning {LLM} Safeguards as Generalizable Safety Policy Reasoners",
author = "Li, Haoran and
Zeng, Jingru and
Chen, Yulin and
Jing, Huihao and
Hu, Wenbin and
Peng, Hao and
Shi, Haochen and
Yang, Xi and
Zeng, Ziqian and
Han, Sirui and
Song, Yangqiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.315/",
pages = "7254--7276",
ISBN = "979-8-89176-459-0",
abstract = "As large language models (LLMs) are integrated into numerous applications, LLMs' safety becomes critical for both application developers and intended users. Currently, great efforts have been made to develop safety benchmarks with fine-grained taxonomies. However, these benchmarks' taxonomies are disparate with different safety policies. Thus, existing safeguards trained on these benchmarks are either coarse-grained to only distinguish between ``safe'' and ``unsafe,'' or constrained by the specified narrow risk taxonomies. To leverage these fine-grained safety policies across multiple safety taxonomies, we propose GSPR, a Generalizable Safety Policy Reasoner to identify unsafe inputs and outputs with violated safety taxonomies and concise explanations. Unlike prior safeguards which only cover a fixed set of risk factors, GSPR incentivizes its reasoning capability with varied safety taxonomies through reinforcement learning. Our GSPR can be trained across multiple safety benchmarks with distinct taxonomies and naturally exhibits powerful generalization ability. We conduct extensive experiments to show that GSPR significantly improves existing safety guardrails' reasoning capabilities for both safety and category prediction tasks. Moreover, GSPR also achieves the least inference token costs with explanations."
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<abstract>As large language models (LLMs) are integrated into numerous applications, LLMs’ safety becomes critical for both application developers and intended users. Currently, great efforts have been made to develop safety benchmarks with fine-grained taxonomies. However, these benchmarks’ taxonomies are disparate with different safety policies. Thus, existing safeguards trained on these benchmarks are either coarse-grained to only distinguish between “safe” and “unsafe,” or constrained by the specified narrow risk taxonomies. To leverage these fine-grained safety policies across multiple safety taxonomies, we propose GSPR, a Generalizable Safety Policy Reasoner to identify unsafe inputs and outputs with violated safety taxonomies and concise explanations. Unlike prior safeguards which only cover a fixed set of risk factors, GSPR incentivizes its reasoning capability with varied safety taxonomies through reinforcement learning. Our GSPR can be trained across multiple safety benchmarks with distinct taxonomies and naturally exhibits powerful generalization ability. We conduct extensive experiments to show that GSPR significantly improves existing safety guardrails’ reasoning capabilities for both safety and category prediction tasks. Moreover, GSPR also achieves the least inference token costs with explanations.</abstract>
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%0 Conference Proceedings
%T GSPR: Aligning LLM Safeguards as Generalizable Safety Policy Reasoners
%A Li, Haoran
%A Zeng, Jingru
%A Chen, Yulin
%A Jing, Huihao
%A Hu, Wenbin
%A Peng, Hao
%A Shi, Haochen
%A Yang, Xi
%A Zeng, Ziqian
%A Han, Sirui
%A Song, Yangqiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F li-etal-2026-gspr
%X As large language models (LLMs) are integrated into numerous applications, LLMs’ safety becomes critical for both application developers and intended users. Currently, great efforts have been made to develop safety benchmarks with fine-grained taxonomies. However, these benchmarks’ taxonomies are disparate with different safety policies. Thus, existing safeguards trained on these benchmarks are either coarse-grained to only distinguish between “safe” and “unsafe,” or constrained by the specified narrow risk taxonomies. To leverage these fine-grained safety policies across multiple safety taxonomies, we propose GSPR, a Generalizable Safety Policy Reasoner to identify unsafe inputs and outputs with violated safety taxonomies and concise explanations. Unlike prior safeguards which only cover a fixed set of risk factors, GSPR incentivizes its reasoning capability with varied safety taxonomies through reinforcement learning. Our GSPR can be trained across multiple safety benchmarks with distinct taxonomies and naturally exhibits powerful generalization ability. We conduct extensive experiments to show that GSPR significantly improves existing safety guardrails’ reasoning capabilities for both safety and category prediction tasks. Moreover, GSPR also achieves the least inference token costs with explanations.
%U https://aclanthology.org/2026.emnlp-main.315/
%P 7254-7276
Markdown (Informal)
[GSPR: Aligning LLM Safeguards as Generalizable Safety Policy Reasoners](https://aclanthology.org/2026.emnlp-main.315/) (Li et al., EMNLP 2026)
ACL
- Haoran Li, Jingru Zeng, Yulin Chen, Huihao Jing, Wenbin Hu, Hao Peng, Haochen Shi, Xi Yang, Ziqian Zeng, Sirui Han, and Yangqiu Song. 2026. GSPR: Aligning LLM Safeguards as Generalizable Safety Policy Reasoners. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 7254–7276, Budapest, Hungary. Association for Computational Linguistics.