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cs.LG2025
Binary and Ternary Quantization Can Enhance Feature Discrimination
Weizhi Lu, Mingrui Chen, Weiyu Li
Quantization is widely applied in machine learning to reduce computational and storage costs for both data and models. Considering that classification tasks are fundamental to the…
cs.CL2025
Data-adaptive Safety Rules for Training Reward Models
Xiaomin Li, Mingye Gao, Zhiwei Zhang +2
Reinforcement Learning from Human Feedback (RLHF) is commonly employed to tailor models to human preferences, especially to improve the safety of outputs from large language models…