RLDP uses a soft actor-critic policy to adapt per-adapter clipping and noise during DP-SGD fine-tuning of LLMs, claiming utility gains and faster convergence, but the privacy proof is internally inconsistent.
Membership inference attacks from first principles
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Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning
RLDP uses a soft actor-critic policy to adapt per-adapter clipping and noise during DP-SGD fine-tuning of LLMs, claiming utility gains and faster convergence, but the privacy proof is internally inconsistent.