4 papers
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
Yi Chen, Rushuai Yang, Qiang Chen +2
Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints. These feature…
ConMem: Structured Memory-Guided Adaptation in Training-Free Multi-Agent Systems
Zhixun Tan, Qiang Chen, Tairan Huang +2
Recent advances have improved the adaptive capabilities of LLM-based multi-agent systems (MAS) through memory-, skill-, and learning-based approaches, yet these approaches remain c…
Supervised Optimism Correction: Be Confident When LLMs Are Sure
Junjie Zhang, Rushuai Yang, Shunyu Liu +5
In this work, we establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning under the token-level Markov decision process, revealing…
Constrained Ensemble Exploration for Unsupervised Skill Discovery
Chenjia Bai, Rushuai Yang, Qiaosheng Zhang +4
Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning useful behaviors via reward-free per-training. Existing methods for unsupervised RL mainly condu…