6 citations · 6 across the 5 of their papers we have counts for
5 papers · 1 filter
On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners
David Mguni, Julian Ma, Jun Wang
Large Language Models (LLMs) are frequently portrayed as general-purpose solvers capable of solving arbitrary tasks. We argue that this view overlooks a fundamental constraint: lan…
All Language Models Large and Small
Zhixun Chen, Yali Du, David Mguni
Many leading language models (LMs) use high-intensity computational resources both during training and execution. This poses the challenge of lowering resource costs for deployment…
Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models
Xue Yan, Yan Song, Xinyu Cui +4
Large language models (LLMs) demonstrate their promise in tackling complicated practical challenges by combining action-based policies with chain of thought (CoT) reasoning. Having…
ChessGPT: Bridging Policy Learning and Language Modeling
Xidong Feng, Yicheng Luo, Ziyan Wang +6
When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment,…
Multi-Agent Determinantal Q-Learning
Yaodong Yang, Ying Wen, Liheng Chen +4
Centralized training with decentralized execution has become an important paradigm in multi-agent learning. Though practical, current methods rely on restrictive assumptions to dec…