Reward-Driven Learning under Prompt-Level Differential Privacy

Excerpt

arXiv:2610.07212v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains a language model on problems that may themselves be confidential, and the trained model can reveal which problems it saw. We study RLVR under prompt-level differential privacy: the released weights must be ({\epsilon},{\delta})-differentially private with respect to the presence of any one training problem. Taking the group of responses to one prompt as the privacy record, our method aggr