13 citations · 20 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Mental Modeling of Reinforcement Learning Agents by Language Models
Wenhao Lu, Xufeng Zhao, Josua Spisak +2
Can emergent language models faithfully model the intelligence of decision-making agents? Though modern language models exhibit already some reasoning ability, and theoretically ca…
Causal State Distillation for Explainable Reinforcement Learning
Wenhao Lu, Xufeng Zhao, Thilo Fryen +4
Reinforcement learning (RL) is a powerful technique for training intelligent agents, but understanding why these agents make specific decisions can be quite challenging. This lack…
A Closer Look at Reward Decomposition for High-Level Robotic Explanations
Wenhao Lu, Xufeng Zhao, Sven Magg +3
Explaining the behaviour of intelligent agents learned by reinforcement learning (RL) to humans is challenging yet crucial due to their incomprehensible proprioceptive states, vari…
Internally Rewarded Reinforcement Learning
Mengdi Li, Xufeng Zhao, Jae Hee Lee +2
We study a class of reinforcement learning problems where the reward signals for policy learning are generated by an internal reward model that is dependent on and jointly optimize…