In reinforcement learning (RL), agents sequentially interact with changing environments while aiming to maximize the obtained rewards.

Autoformalization is the task of translating natural language materials into machine-verifiable formalisations. Progress in autoformalization research is hindered by the lack of a sizeable dataset consisting of informal-formal pairs expressing the same essence.

We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP).

Among all the contributing factors, the quality and selection of data is becoming increasingly recognized for its importance in training LLMs effectively.