aiqbench analysis
This research addresses the optimization trade-off between safety filtering and model utility by introducing Boundary Guidance, a reinforcement learning technique that steers generative models away from classifier decision margins. By avoiding samples near the boundary, this method reduces both false positives and false negatives while maintaining robust performance across jailbreak attempts and long-context scenarios. The findings offer a practical refinement for deploying safe LLMs without sacrificing generation quality or succumbing to adversarial prompts.
Don't Walk the Line: Boundary Guidance for Filtered Generation
arXiv:2510.11834v3 Announce Type: replace-cross Abstract: Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs. A common strategy is to fine-tune the generator to reduce the probability of being filtered, but this can be suboptimal: it often pushes the model toward producing samples near the classifier's decision boundary, increasing both false positives and false negatives. We propose Boundary Guidance, a reinforcement learning fine-tuning method that explicitly steers generation away from the classifier's margin. On a benchmark of jailbreak, ambiguous, and longcontext prompts, Boundary Guidance improves both the safety and the utility of outputs, as judged by LLM-as-a-Judge evaluations. Comprehensive ablations across model scales and reward designs demonstrate the robustness of our approach.
Abstract:Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs. A common strategy is to fine-tune the generator to reduce the probability of being filtered, but this can be suboptimal: it often pushes the model toward producing samples near the classifier's decision boundary, increasing both false positives and false negatives. We propose Boundary Guidance, a reinforcement learning fine-tuning method that explicitly steers generation away from the classifier's margin. On a benchmark of jailbreak, ambiguous, and longcontext prompts, Boundary Guidance improves both the safety and the utility of outputs, as judged by LLM-as-a-Judge evaluations. Comprehensive ablations across model scales and reward designs demonstrate the robustness of our approach.
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2510.11834 [cs.LG]
(or arXiv:2510.11834v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2510.11834 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 207 words
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