RePolicy：学习选择并应用安全策略的 Agent 安全防护方法

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Abstract:Agent safeguards must determine whether an agent's behavior violates the safety rules of the current task, environment, and user. Because these rules vary across contexts, a safeguard needs to select the relevant policy before judging the behavior. Existing policy-aware safeguards can apply a supplied policy, but generally assume that the correct policy is already known. They therefore do not learn how to select a policy from a library, even though an incorrect choice can lead to an incorrect safety judgment. We introduce RePolicy, a safeguard that jointly learns policy selection and safety assessment. Given an agent trajectory and a set of candidate policies, RePolicy selects the policy that best matches the situation, applies its requirements, and predicts whether the behavior is safe. We construct PolicyTraj-20K, a dataset of more than 20,000 agent trajectories annotated with applicable policies, safety labels, and explanations. We train RePolicy with supervised demonstrations followed by reinforcement learning, while randomizing policy order, identifiers, and distractors to reduce shortcut learning. Across six agent-safety benchmarks and 19 baselines, RePolicy-4B achieves leading overall safety-detection performance and improves policy-selection accuracy. These results show that safeguards can learn which safety requirements to use for assessing agent behavior.

Subjects:	Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
Cite as:	arXiv:2608.24275 [cs.AI]
	(or arXiv:2608.24275v3 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2608.24275 arXiv-issued DOI via DataCite

Submission history

From: Houcheng Jiang [view email]
[v1] Tue, 25 Aug 2026 09:01:33 UTC (4,146 KB)
[v2] Thu, 27 Aug 2026 06:48:15 UTC (4,145 KB)
[v3] Thu, 29 Oct 2026 10:16:40 UTC (2,681 KB)