5 papers
HIPO: Instruction Hierarchy via Constrained Reinforcement Learning
Keru Chen, Jun Luo, Sen Lin +4
Hierarchical Instruction Following (HIF) refers to the problem of prompting large language models with a priority-ordered stack of instructions. Standard methods like RLHF and DPO…
Online Finetuning Decision Transformers with Pure RL Gradients
Junkai Luo, Yinglun Zhu
Decision Transformers (DTs) have emerged as a powerful framework for sequential decision making by formulating offline reinforcement learning (RL) as a sequence modeling problem. H…
Shapley-Coop: Credit Assignment for Emergent Cooperation in Self-Interested LLM Agents
Yun Hua, Haosheng Chen, Shiqin Wang +3
Large Language Models (LLMs) show strong collaborative performance in multi-agent systems with predefined roles and workflows. However, in open-ended environments lacking coordinat…
Learning Virtual Machine Scheduling in Cloud Computing through Language Agents
JieHao Wu, Ziwei Wang, Junjie Sheng +3
In cloud services, virtual machine (VM) scheduling is a typical Online Dynamic Multidimensional Bin Packing (ODMBP) problem, characterized by large-scale complexity and fluctuating…
LOPT: Learning Optimal Pigovian Tax in Sequential Social Dilemmas
Yun Hua, Shang Gao, Wenhao Li +5
In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how othe…