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arXiv Machine Learning By Chao Shang, Wanyu Du, Yi Zhang, Sandesh Swamy

AgentPolicyForge: From Executable Environments to Policy Benchmarks and Adaptive Guards

Read the original on arXiv Machine Learning →

AgentPolicyForge is a framework that leverages executable environments to build policy benchmarks and iteratively train policy guards for task agents. It introduces PolicyGym, a dataset covering ten policy families with 698 training scenarios and 254 human‑reviewed test scenarios, where tool calls are executed and outcomes recorded to assess compliance. The framework supports an attacker‑generated request loop that fine‑tunes guards on balanced, environment‑verified examples, leading to a 40‑44% reduction in attack success rates compared to policy prompting alone.

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