Improving Reward Models with Proximal Policy Exploration for Preference-Based Reinforcement Learning
Sep 26, 2025
Squeeze the Soaked Sponge: Efficient Off-policy Reinforcement Finetuning for Large Language Model
This paper introduces an efficient method for finetuning Large Language Models (LLMs) using off-policy reinforcement learning, aiming to improve performance while minimizing computational resources.
Jul 9, 2025
Optimizing Reward Models with Proximal Policy Exploration in Preference-Based Reinforcement Learning
Jul 1, 2024
Ovd-explorer: Optimism should not be the sole pursuit of exploration in noisy environments
We propose Optimistic Value Distribution Explorer (OVD-Explorer) to achieve a noise-aware optimistic exploration for continuous control.
May 1, 2024
A trajectory perspective on the role of data sampling techniques in offline reinforcement learning
Organizing samples in a trajective manner can improve the learning efficiency for offline RL algorithms.
May 1, 2024
Enhancing robotic manipulation with AI feedback from multimodal large language models
Feb 1, 2024
vMFER: Von Mises-Fisher experience resampling based on uncertainty of gradient directions for policy improvement
Jan 1, 2024
Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback
Jan 1, 2024
ENOTO: improving offline-to-online reinforcement learning with Q-ensembles
Jan 1, 2024
OSCAR: OOD State-Conservative Offline Reinforcement Learning for Sequential Decision Making
Jan 1, 2023