2 citations · 3 across the 6 of their papers we have counts for
6 papers
ATLaS: Agent Tuning via Learning Critical Steps
Zhixun Chen, Ming Li, Yuxuan Huang +3
Large Language Model (LLM) agents have demonstrated remarkable generalization capabilities across multi-domain tasks. Existing agent tuning approaches typically employ supervised f…
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?
Yudi Zhang, Lu Wang, Meng Fang +8
Distilling large language models (LLMs) typically involves transferring the teacher model's responses through supervised fine-tuning (SFT). However, this approach neglects the pote…
RuAG: Learned-rule-augmented Generation for Large Language Models
Yudi Zhang, Pei Xiao, Lu Wang +11
In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer…
Safe Multi-agent Reinforcement Learning with Natural Language Constraints
Ziyan Wang, Meng Fang, Tristan Tomilin +2
The role of natural language constraints in Safe Multi-agent Reinforcement Learning (MARL) is crucial, yet often overlooked. While Safe MARL has vast potential, especially in field…
Learning to Discuss Strategically: A Case Study on One Night Ultimate Werewolf
Xuanfa Jin, Ziyan Wang, Yali Du +3
Communication is a fundamental aspect of human society, facilitating the exchange of information and beliefs among people. Despite the advancements in large language models (LLMs),…
MACCA: Offline Multi-agent Reinforcement Learning with Causal Credit Assignment
Ziyan Wang, Yali Du, Yudi Zhang +2
Offline Multi-agent Reinforcement Learning (MARL) is valuable in scenarios where online interaction is impractical or risky. While independent learning in MARL offers flexibility a…