Learning Efficient Guardrails for Compliance
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866917508618911744 |
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| author | Wen, Xiaofei Mo, Wenjie Jacky Xie, Yanan Qi, Peng Chen, Muhao |
| author_facet | Wen, Xiaofei Mo, Wenjie Jacky Xie, Yanan Qi, Peng Chen, Muhao |
| contents | Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adhere to real-world policies remains critically underexplored compared to standard safety objectives. To address this gap, we introduce PolicyGuardBench, a benchmark of 60k policy-trajectory pairs designed to evaluate compliance through both full-trajectory and novel prefix-based violation detection tasks. Using this dataset, we train PolicyGuard, a lightweight guardrail model that achieves strong detection accuracy while maintaining high inference efficiency. Notably, our model demonstrates robust generalization capabilities, preserving high performance even on unseen domains. These contributions establish a comprehensive framework for studying policy compliance, showing that accurate and generalizable guardrails are feasible at small scales. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03485 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Efficient Guardrails for Compliance Wen, Xiaofei Mo, Wenjie Jacky Xie, Yanan Qi, Peng Chen, Muhao Artificial Intelligence I.2.7 Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adhere to real-world policies remains critically underexplored compared to standard safety objectives. To address this gap, we introduce PolicyGuardBench, a benchmark of 60k policy-trajectory pairs designed to evaluate compliance through both full-trajectory and novel prefix-based violation detection tasks. Using this dataset, we train PolicyGuard, a lightweight guardrail model that achieves strong detection accuracy while maintaining high inference efficiency. Notably, our model demonstrates robust generalization capabilities, preserving high performance even on unseen domains. These contributions establish a comprehensive framework for studying policy compliance, showing that accurate and generalizable guardrails are feasible at small scales. |
| title | Learning Efficient Guardrails for Compliance |
| topic | Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2510.03485 |