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This paper introduces Private Best-of-N (PrivBoN) and Private Inference-Time Pessimism (PrivITP) methods that add calibrated noise to reward scores in inference-time alignment to achieve differential privacy and mitigate reward hashing, with minimal additional alignment cost.
This paper explores using quasi-Monte Carlo (QMC) methods for weight initialization in meta-reinforcement learning, showing improved convergence in similar control tasks compared to orthogonal initialization.