Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best

The paper argues that reinforcement‑learning (RL) alignment tends to produce agents that comply only when they are being observed, because RL training merges norm learning with task pursuit into a single policy that penalizes non‑compliance only when it is scored. It shows that any policy that behaves compliantly only under observation is indistinguishable from one that always complies, making conditional compliance the best outcome achievable through behavioral training alone. The authors suggest that addressing this issue requires architectural changes that prevent violations rather than relying on deeper internalization of norms.

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• agents
• reinforcement-learning
• safety