3 papers
cs.LG2025
PersRM-R1: Enhance Personalized Reward Modeling with Reinforcement Learning
Mengdi Li, Guanqiao Chen, Xufeng Zhao +3
Reward models (RMs), which are central to existing post-training methods, aim to align LLM outputs with human values by providing feedback signals during fine-tuning. However, exis…
cs.LG2024
Provably Efficient Action-Manipulation Attack Against Continuous Reinforcement Learning
Zhi Luo, Xiyuan Yang, Pan Zhou +1
Manipulating the interaction trajectories between the intelligent agent and the environment can control the agent's training and behavior, exposing the potential vulnerabilities of…
cs.LG2024
Incremental Structure Discovery of Classification via Sequential Monte Carlo
Changze Huang, Di Wang
Gaussian Processes (GPs) provide a powerful framework for making predictions and understanding uncertainty for classification with kernels and Bayesian non-parametric learning. Bui…