20 citations · 23 across the 4 of their papers we have counts for
4 papers
Principled Data Selection for Alignment: The Hidden Risks of Difficult Examples
Chengqian Gao, Haonan Li, Liu Liu +3
The alignment of large language models (LLMs) often assumes that using more clean data yields better outcomes, overlooking the match between model capacity and example difficulty.…
Hard-Thresholding Meets Evolution Strategies in Reinforcement Learning
Chengqian Gao, William de Vazelhes, Hualin Zhang +2
Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari. Notably,…
Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation
Chengqian Gao, Ke Xu, Liu Liu +3
A promising paradigm for offline reinforcement learning (RL) is to constrain the learned policy to stay close to the dataset behaviors, known as policy constraint offline RL. Howev…
Value Penalized Q-Learning for Recommender Systems
Chengqian Gao, Ke Xu, Kuangqi Zhou +4
Scaling reinforcement learning (RL) to recommender systems (RS) is promising since maximizing the expected cumulative rewards for RL agents meets the objective of RS, i.e., improvi…