Don't Walk the Line: Boundary Guidance for Filtered Generation

MCML Authors

Sarah Ball

→ Group Frauke Kreuter
 Social Data Science and AI

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 and ambiguous 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.

inproceedings BH26a

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ICML 2026

43rd International Conference on Machine Learning. Seoul, South Korea, Jul 06-11, 2026. To be published. Preprint available.

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Authors

S. Ball • A. Haupt

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Links

arXiv

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Research Area

 C4 | Computational Social Sciences