1 citations · 3 across the 20 of their papers we have counts for
7 papers · 1 filter
Optimal Transport for LLM Reward Modeling from Noisy Preference
Licheng Pan, Haochen Yang, Haoxuan Li +8
Reward models are fundamental to Reinforcement Learning from Human Feedback (RLHF), yet real-world datasets are inevitably corrupted by noisy preference. Conventional training obje…
Real-Time Generative Policy via Langevin-Guided Flow Matching for Autonomous Driving
Tianze Zhu, Yinuo Wang, Wenjun Zou +6
Reinforcement learning (RL) is a fundamental methodology in autonomous driving systems, where generative policies exhibit considerable potential by leveraging their ability to mode…
PrivacyCD: Hierarchical Unlearning for Protecting Student Privacy in Cognitive Diagnosis
Mingliang Hou, Yinuo Wang, Teng Guo +6
The need to remove specific student data from cognitive diagnosis (CD) models has become a pressing requirement, driven by users' growing assertion of their "right to be forgotten"…
One Filters All: A Generalist Filter for State Estimation
Shiqi Liu, Wenhan Cao, Chang Liu +3
Estimating hidden states in dynamical systems, also known as optimal filtering, is a long-standing problem in various fields of science and engineering. In this paper, we introduce…
Distributional Soft Actor-Critic with Diffusion Policy
Tong Liu, Yinuo Wang, Xujie Song +6
Reinforcement learning has been proven to be highly effective in handling complex control tasks. Traditional methods typically use unimodal distributions, such as Gaussian distribu…
Enhanced DACER Algorithm with High Diffusion Efficiency
Yinuo Wang, Likun Wang, Mining Tan +11
Due to their expressive capacity, diffusion models have shown great promise in offline RL and imitation learning. Diffusion Actor-Critic with Entropy Regulator (DACER) extended thi…
Diffusion Actor-Critic with Entropy Regulator
Yinuo Wang, Likun Wang, Yuxuan Jiang +8
Reinforcement learning (RL) has proven highly effective in addressing complex decision-making and control tasks. However, in most traditional RL algorithms, the policy is typically…