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cs.LG2026
Fisher Decorator: Refining Flow Policy via a Local Transport Map
Xiaoyuan Cheng, Haoyu Wang, Wenxuan Yuan +4
Recent advances in flow-based offline reinforcement learning (RL) have achieved strong performance by parameterizing policies via flow matching. However, they still face critical t…
cs.LG2025
Learning Instruction-Following Policies through Open-Ended Instruction Relabeling with Large Language Models
Zhicheng Zhang, Ziyan Wang, Yali Du +1
Developing effective instruction-following policies in reinforcement learning remains challenging due to the reliance on extensive human-labeled instruction datasets and the diffic…
cs.LG2024
Natural Language Reinforcement Learning
Xidong Feng, Bo Liu, Yan Song +7
Artificial intelligence progresses towards the "Era of Experience," where agents are expected to learn from continuous, grounded interaction. We argue that traditional Reinforcemen…