BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate
Deploying guardrails for custom policies remains challenging, as generic safety models fail to capture task-specific requirements, while prompting LLMs suffers from inconsistent boundary-case performance and high inference costs. Training custom classifiers achieves both accuracy and efficiency, yet demands substantial labeled data that is costly to obtain. We present BARRED (Boundary Alignment Refinement through REflection and Debate), a framework for generating faithful and diverse synthetic training data using only a task description and a small set of unlabeled examples. Our approach decomposes the domain space into dimensions to ensure comprehensive coverage, and employs multi-agent ...
| Authors | Arnon Mazza, Elad Levi |
|---|---|
| Primary category | cs.CL |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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