2 citations · 2 across the 8 of their papers we have counts for
9 papers
DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
Masahiro Suzuki, Bohui Xia, Hiroto Yamamoto +1
Small-scale data is a critical problem in time-series forecasting tasks. Data augmentation is an effective strategy for this task, but it has a limitation in generating meaningful…
Principled RL for Flow Matching Emerges from the Chunk-level Policy Optimization
Yifu Luo, Haoyuan Sun, Xinhao Hu +12
Recent Progress in post-training flow matching for text-to-image (T2I) generation with Group Relative Policy Optimization (GRPO) has demonstrated strong potential. However, it is h…
Reinforcement Learning Meets Masked Generative Models: Mask-GRPO for Text-to-Image Generation
Yifu Luo, Xinhao Hu, Keyu Fan +6
Reinforcement learning (RL) has garnered increasing attention in text-to-image (T2I) generation. However, most existing RL approaches are tailored to either diffusion models or aut…
Reinforcement Fine-Tuning Powers Reasoning Capability of Multimodal Large Language Models
Haoyuan Sun, Jiaqi Wu, Bo Xia +7
Standing in 2025, at a critical juncture in the pursuit of Artificial General Intelligence (AGI), reinforcement fine-tuning (RFT) has demonstrated significant potential in enhancin…
Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through -divergence Minimization
Haoyuan Sun, Bo Xia, Yongzhe Chang +1
Direct Preference Optimization (DPO) has recently expanded its successful application from aligning large language models (LLMs) to aligning text-to-image models with human prefere…
Trajectory Planning for Teleoperated Space Manipulators Using Deep Reinforcement Learning
Bo Xia, Xianru Tian, Bo Yuan +3
Trajectory planning for teleoperated space manipulators involves challenges such as accurately modeling system dynamics, particularly in free-floating modes with non-holonomic cons…
DEER: A Delay-Resilient Framework for Reinforcement Learning with Variable Delays
Bo Xia, Yilun Kong, Yongzhe Chang +4
Classic reinforcement learning (RL) frequently confronts challenges in tasks involving delays, which cause a mismatch between received observations and subsequent actions, thereby…
A Method on Searching Better Activation Functions
Haoyuan Sun, Zihao Wu, Bo Xia +5
The success of artificial neural networks (ANNs) hinges greatly on the judicious selection of an activation function, introducing non-linearity into network and enabling them to mo…
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement Learning
Zhecheng Yuan, Guozheng Ma, Yao Mu +5
One of the key challenges in visual Reinforcement Learning (RL) is to learn policies that can generalize to unseen environments. Recently, data augmentation techniques aiming at en…