arXiv:2606.16808v1 Announce Type: new Abstract: While Large Reasoning Models (LRMs) excel at complex tasks, they remain highly vulnerable to sophisticated jailbreaks and direct harmful queries. To address this vulnerability, prior works depend heavily on external manual data annotation for safety alignment. However, we observe that LRMs can inherently identify safety risks when being re-presented with original queries alongside their own reasoning trajectories -- a capability we term Latent Safety Awareness. To leverage this safety awareness, we first employ Supervised Fine-Tuning (SFT) to exp
The proliferation of Large Language Models (LLMs) and their deployment in sensitive applications necessitates robust safety mechanisms beyond manual annotation, leading to novel approaches in self-correction.
This development indicates a move towards more autonomous and inherent safety alignment for AI, reducing reliance on labor-intensive and potentially subjective external oversight, which is critical for scaling AI deployment.
Safety alignment for large reasoning models may shift from predominantly external, data-driven methods to incorporating models' 'latent safety awareness,' potentially accelerating secure AI integration.
- · AI developers
- · Organizations deploying LLMs
- · AI safety research
- · External annotation providers for safety
- · AI jailbreakers
Large Reasoning Models become more inherently robust against harmful queries and jailbreaks without constant external intervention.
Reduced costs and increased efficiency in deploying powerful AI systems across various sensitive sectors due to improved internal safety.
The development of truly autonomous AI agents capable of self-policing their outputs for harmful content, accelerating the 'AI agents' narrative meaningfully.
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Read at arXiv cs.AI