Robust and Efficient Guardrails with Latent Reasoning

• URL: http://arxiv.org/abs/2605.29068v1
• Date: Wed, 27 May 2026 20:15:22 GMT
• Title: Robust and Efficient Guardrails with Latent Reasoning
• Abstract summary: Reasoning-based guardrails incur substantial query latency and token overhead.<n>We propose COLAGUARD, a guardrail model that transfers multi-step safety reasoning into a continuous latent space.<n>Our results suggest latent reasoning offers a practical alternative to explicit rationale generation for deployable guardrails.
• Score: 23.837448508316513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Maintaining the safety of large language models (LLMs) is crucial as they are increasingly deployed in real-world applications. Existing safety guardrails typically rely on single-pass classification or, more recently, distilled reasoning. Reasoning-based guardrails significantly outperform classification-only baselines, but they incur substantial query latency and token overhead that make them impractical for highthroughput deployment. To address this challenge, we propose COLAGUARD, a guardrail model that transfers multi-step safety reasoning into a continuous latent space through a stage-wise training curriculum, enabling direct hidden-state propagation at inference. Evaluated on ten prompt- and response-moderation settings spanning eight safety benchmarks, COLAGUARD improves macro-F1 by 8.24 points over Llama Guard 3 and matches our explicit reasoning baseline, GuardReasoner, in macroF1 while delivering a 12.9X speedup and 22.4X reduction in token usage. Our results suggest that latent reasoning offers a practical alternative to explicit rationale generation for deployable guardrails, jointly improving safety robustness and inference efficiency rather than treating them as competing objectives.

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