Safety Through Reasoning: An Empirical Study of Reasoning Guardrail Models (2025)

Abstract: Reasoning-based language models have demonstrated strong performance across various domains, with the most notable gains seen in mathematical and coding tasks. Recent research has shown that reasoning also offers significant benefits for LLM safety and guardrail applications. In this work, we conduct a comprehensive analysis of training reasoning-based guardrail models for content moderation, with an emphasis on generalization to custom safety policies at inference time. Our study focuses on two key dimensions: data efficiency and inference efficiency. On the data front, we find that reasoning-based models exhibit strong sample efficiency, achieving competitive performance with significantly fewer training examples than their non-reasoning counterparts. This unlocks the potential to repurpose the remaining data for mining high-value, difficult samples that further enhance model performance. On the inference side, we evaluate practical trade-offs by introducing reasoning budgets, examining the impact of reasoning length on latency and accuracy, and exploring dual-mode training to allow runtime control over reasoning behavior. Our findings will provide practical insights for researchers and developers to effectively and efficiently train and deploy reasoning-based guardrails models in real-world systems.

## introduction with large language models (llms) achieving steady improvements on a wide range of nlp tasks, they have become the most important language technology used daily by billions of users globally @xcite as most llms are built as autoregressive transformers trained on large-scale textual corpora, alignment through supervised finetuning and reinforcement learning remains the primary approach to steer model behavior toward human-aligned values and preferences @xcite @xcite . while alignment instills basic generic safety and security in the model weights at training @xcite , most applications that deploy llms in production need to use an additional layer of guardrails @xcite . guardrails for llms can be implemented via classifiers @xcite , inference-time steering @xcite , or structured reasoning pipelines @xcite -the former being the most widely used due to their simplicity and effectiveness. they support tasks like content filtering @xcite , jailbreak prevention @xcite , and dialogue coherence @xcite . inspired by advances in reasoning for structured tasks @xcite , recent work has introduced reasoning-augmented guard models @xcite , showing improved robustness to jailbreaks and adversarial attacks. in this paper, we present a comprehensive investigation into the role of reasoning in enhancing the performance of guardrail models. rather than proposing new reasoning-based classifiers, our focus is on understanding how to effectively train and deploy such models using controlled ablations and experiments. first, we demonstrate that reasoningbased guard models exhibit significantly greater data efficiency, achieving competitive results using only a fraction of the data required by their non-reasoning counterparts. second, we explore methods to reduce inference latency, showing that limiting reasoning trace length and employing dualmode models (reasoning and non-reasoning) can maintain performance while improving runtime efficiency. third, we identify a gap in model performance when adapting to custom safety policies and propose augmenting training with dialogue moderation data to address this limitation. finally, we introduce a strategy to mine difficult, decisionboundary samples from large safety datasets, offer-ing an effective pathway for further refining model accuracy and robustness. ## method to construct effective reasoning-based guardrail models, we begin with existing safety and topicfollowing datasets that lack explicit reasoning traces. first, we use a strong teacher model to generate reasoning traces for the datasets, optionally producing traces of varying lengths to reduce latency during inference. these traces are filtered for quality, and a small, high-quality subset n ≪ m , where m is the size of the original dataset, is used to efficiently train compact reasoning models. once the initial model is trained, we extend it to support both reasoning and non-reasoning inference modes using dual-mode training. to further improve performance, we identify difficult samples which are near the decision boundaries using model disagreement patterns, and use these in a second-stage fine-tuning process through continued supervised fine-tuning or preference optimization (ex., dpo, grpo). finally, we incorporate dialogue moderation (topic-following) data to enhance adaptability to custom safety policies and dialogue moderation tasks, ensuring broader generalization to novel safety policies which are applicable to real-world deployment scenarios. figure 1 shows the overview of the proposed methodology and experimental pipeline. ## experimental setup this section details the construction of datasets, reasoning traces, and model configurations used in the study. to enable consistency and ensure reliable comparisons to address the different research questions, we fix certain design choices to minimize variability in the experimental components. ## adaptation to custom safety policies in real-world settings, safety requirements are often context-specific and require adaptation at inference without retraining. we evaluate this adaptability using benchmarks that test generalization to dynamic or persona-conditioned safety policies. rq8: can reasoning-based guard models effectively generalize to novel, custom safety policies at inference time? ## inference time concerns figure 4 shows average inference times across guard models, measured on 8 a100 gpus using vllm with dynamic batching. non-reasoning models have the lowest latency (0.019s). reasoning increases latency, with unrestricted models incurring a 170.7% overhead. constraining reasoning to one sentence reduces the overhead to 34.9%, offering a practical trade-off. these results also highlight the advantage of dual-mode models: by default, models can operate in non-reasoning mode to match baseline latency, and reasoning can be activated selectively when higher performance or adaptation to custom policies is needed. ## conclusion in this study, we present a systematic evaluation of reasoning in training guard models. our findings confirm that several benefits observed in other reasoning-heavy domains extend to safety tasksstrong data efficiency and dual-mode models that support both reasoning and non-reasoning inference. we also show that reasoning enables better generalization to custom safety policies, especially when augmented with dialogue moderation data. however, not all reasoning techniques translate effectively -for instance, longer reasoning traces offer limited benefit, and methods like grpo show minimal added value under our setup. overall, our results offer practical guidance on how to train and deploy reasoning-based guard models efficiently.