2 citations · 3 across the 13 of their papers we have counts for
15 papers
Beyond Isolation: Unlocking Reinforcement Learning Component Synergy for Sample-Efficient Continuous Control
Qi Zhao, Guozheng Ma, Yilun Kong +9
Reinforcement learning systems are significantly more complex than other machine learning paradigms due to inherent properties, causing RL system design to jointly account for many…
Bridging Perception and Action: A Lightweight Multimodal Meta-Planner Framework for Robust Earth Observation Agents
Jinghui Xu, Boyi Shangguan, Mengke Zhu +10
Autonomous Earth Observation (EO) agents are transitioning from passive perception to complex, multi-step task execution. However, current architectures that integrate planning and…
Co-Evolutionary Multi-Modal Alignment via Structured Adversarial Evolution
Guoxin Shi, Haoyu Wang, Zaihui Yang +2
Adversarial behavior plays a central role in aligning large language models with human values. However, existing alignment methods largely rely on static adversarial settings, whic…
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…
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…
Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning
Kai Qin, Jiaqi Wu, Jianxiang He +8
As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention. LLM un…
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…