9 papers
Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance
Jingwei Zhang, Haoyu Lei, Zijin Feng +2
Although diffusion models have revolutionized continuous domains like image synthesis through high quality generations and controllable guidance mechanisms, bringing this controlla…
FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models
Haoyang Li, Liang Wang, Siyu Zhou +5
CLIP-based prompt tuning enables pretrained Vision-Language Models (VLMs) to efficiently adapt to downstream tasks. Although existing studies have made significant progress, they p…
Stabilizing Reinforcement Learning for Diffusion Language Models
Jianyuan Zhong, Kaibo Wang, Ding Ding +5
Group Relative Policy Optimization (GRPO) is highly effective for post-training autoregressive (AR) language models, yet its direct application to diffusion large language models (…
CRISP: Compressed Reasoning via Iterative Self-Policy Distillation
Hejian Sang, Yuanda Xu, Zhengze Zhou +3
Reasoning models often generate far more tokens than a task requires, which raises inference cost and can compound errors. We introduce CRISP (Compressed Reasoning via Iterative Se…
HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs
Azim Ospanov, Zijin Feng, Jiacheng Sun +3
Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments. However, purely informal reasoni…
Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture
Shuchen Xue, Tianyu Xie, Tianyang Hu +5
Efficiently scaling Large Language Models (LLMs) necessitates exploring alternatives to dominant autoregressive (AR) methods, with Masked Diffusion Models (MDMs) emerging as candid…
Variational Autoencoding Discrete Diffusion with Enhanced Dimensional Correlations Modeling
Tianyu Xie, Shuchen Xue, Zijin Feng +4
Discrete diffusion models have recently shown great promise for modeling complex discrete data, with masked diffusion models (MDMs) offering a compelling trade-off between quality…
Learning Few-Step Diffusion Models by Trajectory Distribution Matching
Yihong Luo, Tianyang Hu, Jiacheng Sun +2
Accelerating diffusion model sampling is crucial for efficient AIGC deployment. While diffusion distillation methods -- based on distribution matching and trajectory matching -- re…
Adding Additional Control to One-Step Diffusion with Joint Distribution Matching
Yihong Luo, Tianyang Hu, Yifan Song +3
While diffusion distillation has enabled one-step generation through methods like Variational Score Distillation, adapting distilled models to emerging new controls -- such as nove…