6 citations · 7 across the 9 of their papers we have counts for
11 papers
AInfer-PD: Communication-Safe In-Place Prefill-Decode Multiplexing for Distributed MoE Rollouts
Guowei Wang, Chaokun Yang, Zhenxuan Pan +6
Rollout inference often dominates the wall-clock time of large-scale reinforcement learning (RL). In agentic RL, each trajectory alternates between model generation and environment…
Select-to-Act: Hierarchical Reinforcement Learning via Adaptive Language Guidance
Hanping Zhang, Adam Koziak, Yuhong Guo
Reinforcement Learning (RL) has been widely applied to sequential decision-making, yet it often suffers from poor sample efficiency due to costly interactions with the environment.…
Bridging Dynamics Gaps via Diffusion Schrödinger Bridge for Cross-Domain Reinforcement Learning
Hanping Zhang, Yuhong Guo
Cross-domain reinforcement learning (RL) aims to learn transferable policies under dynamics shifts between source and target domains. A key challenge lies in the lack of target-dom…
Diffusion Modulation via Environment Mechanism Modeling for Planning
Hanping Zhang, Yuhong Guo
Diffusion models have shown promising capabilities in trajectory generation for planning in offline reinforcement learning (RL). However, conventional diffusion-based planning meth…
Learning to Clean: Reinforcement Learning for Noisy Label Correction
Marzi Heidari, Hanping Zhang, Yuhong Guo
The challenge of learning with noisy labels is significant in machine learning, as it can severely degrade the performance of prediction models if not addressed properly. This pape…
LLM-Driven Policy Diffusion: Enhancing Generalization in Offline Reinforcement Learning
Hanping Zhang, Yuhong Guo
Reinforcement Learning (RL) is known for its strong decision-making capabilities and has been widely applied in various real-world scenarios. However, with the increasing availabil…
Skill-based Safe Reinforcement Learning with Risk Planning
Hanping Zhang, Yuhong Guo
Safe Reinforcement Learning (Safe RL) aims to ensure safety when an RL agent conducts learning by interacting with real-world environments where improper actions can induce high co…
Safety Modulation: Enhancing Safety in Reinforcement Learning through Cost-Modulated Rewards
Hanping Zhang, Yuhong Guo
Safe Reinforcement Learning (Safe RL) aims to train an RL agent to maximize its performance in real-world environments while adhering to safety constraints, as exceeding safety vio…
Skill-Enhanced Reinforcement Learning Acceleration from Heterogeneous Demonstrations
Hanping Zhang, Yuhong Guo
Learning from Demonstration (LfD) is a well-established problem in Reinforcement Learning (RL), which aims to facilitate rapid RL by leveraging expert demonstrations to pre-train t…
Safe Reinforcement Learning with Contrastive Risk Prediction
Hanping Zhang, Yuhong Guo
As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled t…
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation
Hanping Zhang, Yuhong Guo
The generalization gap in reinforcement learning (RL) has been a significant obstacle that prevents the RL agent from learning general skills and adapting to varying environments.…