1 citations · 1 across the 5 of their papers we have counts for
5 papers
Learn to Match: Two-Sided Matching with Temporally Extended Feedback
Haijing Zong, Yancheng Liang, Boyang Zhou +1
Two-sided matching markets often involve information that unfolds over time through interviews, repeated interaction, learning, and separation. Existing matching models typically r…
Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
Mickel Liu, Liwei Jiang, Yancheng Liang +4
Conventional large language model (LLM) safety alignment relies on a reactive, disjoint loop: attackers exploit a static model, then defenders patch exposed vulnerabilities. This s…
Cross-environment Cooperation Enables Zero-shot Multi-agent Coordination
Kunal Jha, Wilka Carvalho, Yancheng Liang +3
Zero-shot coordination (ZSC), the ability to adapt to a new partner in a cooperative task, is a critical component of human-compatible AI. While prior work has focused on training…
Improving Human-AI Coordination through Online Adversarial Training and Generative Models
Paresh Chaudhary, Yancheng Liang, Daphne Chen +2
Being able to cooperate with diverse humans is an important component of many economically valuable AI tasks, from household robotics to autonomous driving. However, generalizing t…
Learning to Cooperate with Humans using Generative Agents
Yancheng Liang, Daphne Chen, Abhishek Gupta +2
Training agents that can coordinate zero-shot with humans is a key mission in multi-agent reinforcement learning (MARL). Current algorithms focus on training simulated human partne…