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cs.LG2024★ 1 cited
Adaptive Preference Scaling for Reinforcement Learning with Human Feedback
Ilgee Hong, Zichong Li, Alexander Bukharin +4
Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values by learning rewards from human preference data. Due to various reaso…
cs.LG2024★ 1 cited
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
Zi-Hao Qiu, Siqi Guo, Mao Xu +3
The temperature parameter plays a profound role during training and/or inference with large foundation models (LFMs) such as large language models (LLMs) and CLIP models. Particula…
cs.LG2023
Stochastic Approximation Approaches to Group Distributionally Robust Optimization and Beyond
Lijun Zhang, Haomin Bai, Peng Zhao +2
This paper investigates group distributionally robust optimization (GDRO) with the goal of learning a model that performs well over different distributions. First, we formulate…