Meng Ding, Mingxi Lei, Jie Zhang, Jinyan Liu, Di Wang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.30099 · open-access PDF
Reinforcement Learning with Human Feedback (RLHF) は、大規模な言語モデルと人間の価値観を調節するための中心的なパラダイムとなっています。しかしながら、敏感な好みデータへの依存は、ユーザーの情報の漏洩を防ぐために厳格な差別プライバシー(DP)保証を必要とします。既存のプライベートアライアンス方法は、通常、密集した報酬パラダイメーションを仮定し、環境 {モデルサイズ $d$} に多角的にスケールする推定エラーの境界を生みます。このような依存は、現代の {overparameterized モデル} で禁止されます。この研究では、私たちは \hemp{Private Sparse RLHF} pr
Machine translation (Meta M2M100); the English original follows.
Reinforcement Learning with Human Feedback (RLHF) has become a central paradigm for aligning Large Language Models with human values. However, the reliance on sensitive preference data necessitates rigorous differential privacy (DP) guarantees to prevent user information leakage. Existing private alignment methods typically assume dense reward parameterizations, resulting in estimation error bounds that scale polynomially with the ambient {model size $d$}. Such dependence is prohibitive in modern {overparameterized models} where $d$ is vast. In this work, we study \emph{Private Sparse RLHF} pr
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