1 citations · 1 across the 7 of their papers we have counts for
4 papers · 1 filter
Designing Time Series Experiments in A/B Testing with Transformer Reinforcement Learning
Xiangkun Wu, Qianglin Wen, Yingying Zhang +3
A/B testing has become a gold standard for modern technological companies to conduct policy evaluation. Yet, its application to time series experiments, where policies are sequenti…
rePIRL: Learn PRM with Inverse RL for LLM Reasoning
Xian Wu, Kaijie Zhu, Ying Zhang +2
Process rewards have been widely used in deep reinforcement learning to improve training efficiency, reduce variance, and prevent reward hacking. In LLM reasoning, existing works a…
Virtual Width Networks
Seed, Baisheng Li, Banggu Wu +115
We introduce Virtual Width Networks (VWN), a framework that delivers the benefits of wider representations without incurring the quadratic cost of increasing the hidden size. VWN d…
RepAct: The Re-parameterizable Adaptive Activation Function
Xian Wu, Qingchuan Tao, Shuang Wang
Addressing the imperative need for efficient artificial intelligence in IoT and edge computing, this study presents RepAct, a re-parameterizable adaptive activation function tailor…