2 citations · 3 across the 12 of their papers we have counts for
14 papers
Balancing Performance and Diversity in GRPO Autoregressive Text-to-Image Post-Training
Yuanhao Chiang, Hongbo Duan, Chunru Yang +3
Autoregressive text-to-image (T2I) generation has recently advanced rapidly, yet aligning generated images with human preferences remains challenging. GRPO-style online reinforceme…
UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
Jiaxi Wu, Tiantian Zhang, Yuxing Wang +2
Robust adversarial reinforcement learning has emerged as an effective paradigm for training agents to handle uncertain disturbance in real environments, with critical applications…
Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges
Yuxing Wang, Zhiyu Chen, Tiantian Zhang +5
Brain-body co-evolution enables animals to develop complex behaviors in their environments. Inspired by this biological synergy, embodied co-design (ECD) has emerged as a transform…
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…
Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning
Yifu Luo, Yongzhe Chang, Xueqian Wang
Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. However, existing approaches primarily focu…
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…
Morphology and Behavior Co-Optimization of Modular Satellites for Attitude Control
Yuxing Wang, Jie Li, Cong Yu +5
The emergence of modular satellites marks a significant transformation in spacecraft engineering, introducing a new paradigm of flexibility, resilience, and scalability in space ex…
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…
Probing the Safety Response Boundary of Large Language Models via Unsafe Decoding Path Generation
Haoyu Wang, Bingzhe Wu, Yatao Bian +3
Large Language Models (LLMs) are implicit troublemakers. While they provide valuable insights and assist in problem-solving, they can also potentially serve as a resource for malic…
QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning
Yilun Kong, Hangyu Mao, Qi Zhao +7
Prompt engineering has demonstrated remarkable success in enhancing the performance of large language models (LLMs) across diverse tasks. However, most existing prompt optimization…
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…
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning
Longxiang He, Li Shen, Linrui Zhang +2
Constrained policy search (CPS) is a fundamental problem in offline reinforcement learning, which is generally solved by advantage weighted regression (AWR). However, previous meth…