7 papers
World Models Should Prioritize the Unification of Physical and Social Dynamics
Xiaoyuan Zhang, Chengdong Ma, Yizhe Huang +5
World models, which explicitly learn environmental dynamics to lay the foundation for planning, reasoning, and decision-making, are rapidly advancing in predicting both physical dy…
Empirical Study on Robustness and Resilience in Cooperative Multi-Agent Reinforcement Learning
Simin Li, Zihao Mao, Hanxiao Li +13
In cooperative Multi-Agent Reinforcement Learning (MARL), it is a common practice to tune hyperparameters in ideal simulated environments to maximize cooperative performance. Howev…
Social World Model-Augmented Mechanism Design Policy Learning
Xiaoyuan Zhang, Yizhe Huang, Chengdong Ma +6
Designing adaptive mechanisms to align individual and collective interests remains a central challenge in artificial social intelligence. Existing methods often struggle with model…
Goal Discovery with Causal Capacity for Efficient Reinforcement Learning
Yan Yu, Yaodong Yang, Zhengbo Lu +3
Causal inference is crucial for humans to explore the world, which can be modeled to enable an agent to efficiently explore the environment in reinforcement learning. Existing rese…
Falcon: Fast Visuomotor Policies via Partial Denoising
Haojun Chen, Minghao Liu, Chengdong Ma +8
Diffusion policies are widely adopted in complex visuomotor tasks for their ability to capture multimodal action distributions. However, the multiple sampling steps required for ac…
Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs
Zhaowei Zhang, Fengshuo Bai, Qizhi Chen +5
How to align large language models (LLMs) with user preferences from a static general dataset has been frequently studied. However, user preferences are usually personalized, chang…