3 citations · 5 across the 13 of their papers we have counts for
14 papers
Exploring More, Reasoning Better: Stepwise Risk-Sensitive GRPO for Diffusion Language Models
Yue Yu, Yue YU, Bowen Zuo +3
Diffusion large language models (dLLMs) generate text by denoising a sequence or successive blocks, allowing several tokens to be revealed in parallel. Reinforcement learning with…
Harness Handbook: Making Evolving Agent Harnesses Readable,Navigable, and Editable
Ruhan Wang, Yucheng Shi, Zongxia Li +7
The capability of a modern AI agent depends not only on its foundation model but also on its harness, which constructs prompts, manages state, invokes tools, and coordinates execut…
HiComm: Hierarchical Communication for Multi-agent Reinforcement Learning
Runze Zhao, Dongruo Zhou, Sumit Kumar Jha +2
Cooperative multi-agent reinforcement learning (MARL) often relies on communication to mitigate partial observability, yet most existing protocols treat messages as flat dense vect…
FERA: Uncertainty-Aware Federated Reasoning for Large Language Models
Ruhan Wang, Chengkai Huang, Zhiyong Wang +6
Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across organizations that cannot c…
Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations
Bowen Zuo, Dongruo Zhou, Yinglun Zhu
While scaling test-time compute can substantially improve model performance, existing approaches either rely on static compute allocation or sample from fixed generation distributi…
Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation
Runze Zhao, Yue Yu, Ruhan Wang +2
Continuous-time reinforcement learning (CTRL) provides a natural framework for sequential decision-making in dynamic environments where interactions evolve continuously over time.…
How to Provably Improve Return Conditioned Supervised Learning?
Zhishuai Liu, Yu Yang, Ruhan Wang +2
In sequential decision-making problems, Return-Conditioned Supervised Learning (RCSL) has gained increasing recognition for its simplicity and stability in modern decision-making t…
Federated In-Context Learning: Iterative Refinement for Improved Answer Quality
Ruhan Wang, Zhiyong Wang, Chengkai Huang +5
For question-answering (QA) tasks, in-context learning (ICL) enables language models to generate responses without modifying their parameters by leveraging examples provided in the…
Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
Runze Zhao, Yue Yu, Adams Yiyue Zhu +2
Continuous-time reinforcement learning (CTRL) provides a principled framework for sequential decision-making in environments where interactions evolve continuously over time. Despi…
Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models
Chengkai Huang, Junda Wu, Yu Xia +9
Recent breakthroughs in Large Language Models (LLMs) have led to the emergence of agentic AI systems that extend beyond the capabilities of standalone models. By empowering LLMs to…
Provable Zero-Shot Generalization in Offline Reinforcement Learning
Zhiyong Wang, Chen Yang, John C. S. Lui +1
In this work, we study offline reinforcement learning (RL) with zero-shot generalization property (ZSG), where the agent has access to an offline dataset including experiences from…
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds
Zhiyong Wang, Dongruo Zhou, John C. S. Lui +1
Learning a transition model via Maximum Likelihood Estimation (MLE) followed by planning inside the learned model is perhaps the most standard and simplest Model-based Reinforcemen…
Variance-Dependent Regret Bounds for Non-stationary Linear Bandits
Zhiyong Wang, Jize Xie, Yi Chen +2
We investigate the non-stationary stochastic linear bandit problem where the reward distribution evolves each round. Existing algorithms characterize the non-stationarity by the to…
DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training
Zihao Wang, Rui Zhu, Dongruo Zhou +4
Recent developments have underscored the critical role of \textit{differential privacy} (DP) in safeguarding individual data for training machine learning models. However, integrat…