Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.
Privately aligning language models with reinforcement learning
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On the Sample Complexity of Differentially Private Policy Optimization
Differential privacy in policy optimization adds sample complexity costs that often appear as lower-order terms rather than dominating the bounds.
- Autonomy Reshapes How Personalization Affects Privacy Concerns and Trust in LLM Agents