1 citations · 1 across the 6 of their papers we have counts for
6 papers
Shattering the Autoregressive Curse: Dynamic Epistemic Entropy Orchestrated Erasable Reinforcement Learning for LLMs
Ziliang Wang, Kang An, Faqiang Qian +5
Although reinforcement learning (RL) has expanded the cognitive boundaries of large language models (LLMs), it often remains vulnerable to the autoregressive curse in long-horizon…
MMRPT: MultiModal Reinforcement Pre-Training via Masked Vision-Dependent Reasoning
Xuhui Zheng, Kang An, Ziliang Wang +3
Multimodal pre-training remains constrained by the descriptive bias of image-caption pairs, leading models to favor surface linguistic cues over grounded visual understanding. We i…
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
Ziliang Wang, Kang An, Xuhui Zheng +6
While search-augmented large language models (LLMs) exhibit impressive capabilities, their reliability in complex multi-hop reasoning remains limited. This limitation arises from t…
StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization
Ziliang Wang, Xuhui Zheng, Kang An +4
Efficient multi-hop reasoning requires Large Language Models (LLMs) based agents to acquire high-value external knowledge iteratively. Previous work has explored reinforcement lear…
Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework
Ruijia Wu, Yuhang Wang, Huafeng Shi +3
Denoising diffusion models have shown remarkable potential in various generation tasks. The open-source large-scale text-to-image model, Stable Diffusion, becomes prevalent as it c…
Universal Backdoor Attacks Detection via Adaptive Adversarial Probe
Yuhang Wang, Huafeng Shi, Rui Min +5
Extensive evidence has demonstrated that deep neural networks (DNNs) are vulnerable to backdoor attacks, which motivates the development of backdoor attacks detection. Most detecti…