SAVeR$^2$: Reasoning-based Safety Alignment for Large Reasoning Models via Verifiable Rewards

Authors: Yichen Sun , Qirui Liu , Yan Wang , Jianan Lin , Linbo Jiang , Shiyu Wang , Zhibo Wang , Zhixuan Chu

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Abstract: Large Reasoning Models (LRMs) have achieved impressive performance via explicit Chain-of-Thought (CoT) reasoning, yet this process introduces critical safety risks. We formalize LRM safety objectives as requiring non-malicious reasoning and answers, safety consistency, and explicit intent identification. We argue that existing alignment methods suffer from poor adversarial generalization, a significant ``safety tax'' on reasoning, and unfaithful coarse-grained rewards, primarily due to their neglect of the LRM's intrinsic reasoning capabilities for safety. To address these, we propose \textbf{SAVeR$^2$}, a novel \textbf{S}afety \textbf{A}lignment method via \textbf{R}ewards with \textbf{R}easoning capability. SAVeR$^2$ decomposes the safety objective into four fine-grained verifiable reward signals and utilizes a difficulty-aware data selection strategy to stabilize training. Crucially, we introduce a reasoning-preserving gradient projection mechanism that analytically resolves conflicts between safety and reasoning gradients, effectively eliminating the safety tax. Extensive experiments on models ranging from 1.5B to 671B demonstrate that SAVeR$^2$ achieves superior safety performance and significantly lower over-refusal rates across multiple benchmarks without compromising general reasoning capabilities.

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Artificial Intelligence (cs.AI)

Cite as:

arXiv:2611.07482 [cs.AI]

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arXiv:2611.07482v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.07482

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arXiv-issued DOI via DataCite (pending registration)

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From: Yichen Sun [ view email ]

[v1]

Thu, 5 Nov 2026 05:42:45 UTC (1,054 KB)

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