19 citations · 30 across the 13 of their papers we have counts for
15 papers
MARA: Flow-Matching-Guided Multi-Agent Resource Allocation for Computational Resource Efficient Learning
Hanye Zhao, Muning Wen, Yong Yu +1
Allocating limited computation among concurrent learning tasks is difficult when each task must reach a target loss before a deadline but its required training effort is unknown. E…
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning
Shutong Ding, Ke Hu, Shan Zhong +5
Recent advances in reinforcement learning (RL) have demonstrated the powerful exploration capabilities and multimodality of generative diffusion-based policies. While substantial p…
ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning
Ziyu Wan, Yunxiang Li, Xiaoyu Wen +8
Recent research on Reasoning of Large Language Models (LLMs) has sought to further enhance their performance by integrating meta-thinking -- enabling models to monitor, evaluate, a…
OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models
Jun Wang, Meng Fang, Ziyu Wan +10
In this technical report, we introduce OpenR, an open-source framework designed to integrate key components for enhancing the reasoning capabilities of large language models (LLMs)…
Computing Ex Ante Equilibrium in Heterogeneous Zero-Sum Team Games
Naming Liu, Mingzhi Wang, Xihuai Wang +5
The ex ante equilibrium for two-team zero-sum games, where agents within each team collaborate to compete against the opposing team, is known to be the best a team can do for coord…
RACon: Retrieval-Augmented Simulated Character Locomotion Control
Yuxuan Mu, Shihao Zou, Kangning Yin +4
In computer animation, driving a simulated character with lifelike motion is challenging. Current generative models, though able to generalize to diverse motions, often pose challe…
Reinforcing Language Agents via Policy Optimization with Action Decomposition
Muning Wen, Ziyu Wan, Weinan Zhang +2
Language models as intelligent agents push the boundaries of sequential decision-making agents but struggle with limited knowledge of environmental dynamics and exponentially huge…
Looking Ahead to Avoid Being Late: Solving Hard-Constrained Traveling Salesman Problem
Jingxiao Chen, Ziqin Gong, Minghuan Liu +3
Many real-world problems can be formulated as a constrained Traveling Salesman Problem (TSP). However, the constraints are always complex and numerous, making the TSPs challenging…
Entropy-Regularized Token-Level Policy Optimization for Language Agent Reinforcement
Muning Wen, Junwei Liao, Cheng Deng +3
Large Language Models (LLMs) have shown promise as intelligent agents in interactive decision-making tasks. Traditional approaches often depend on meticulously designed prompts, hi…
InfoRank: Unbiased Learning-to-Rank via Conditional Mutual Information Minimization
Jiarui Jin, Zexue He, Mengyue Yang +4
Ranking items regarding individual user interests is a core technique of multiple downstream tasks such as recommender systems. Learning such a personalized ranker typically relies…
Vision-Language Foundation Models as Effective Robot Imitators
Xinghang Li, Minghuan Liu, Hanbo Zhang +9
Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipul…
Lending Interaction Wings to Recommender Systems with Conversational Agents
Jiarui Jin, Xianyu Chen, Fanghua Ye +5
Recommender systems trained on offline historical user behaviors are embracing conversational techniques to online query user preference. Unlike prior conversational recommendation…
ZSC-Eval: An Evaluation Toolkit and Benchmark for Multi-agent Zero-shot Coordination
Xihuai Wang, Shao Zhang, Wenhao Zhang +4
Zero-shot coordination (ZSC) is a new cooperative multi-agent reinforcement learning (MARL) challenge that aims to train an ego agent to work with diverse, unseen partners during d…
Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: the Past, Present, and Future
Yan Song, He Jiang, Haifeng Zhang +3
Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper, recent years have witnessed an increasing focu…
Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training
Xidong Feng, Ziyu Wan, Muning Wen +4
Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step reaso…