2 citations · 4 across the 9 of their papers we have counts for
11 papers
On-Policy Self-Distillation in Diffusion Models
Wei Zhou, Xiongwei Zhu, Lingdong Kong +14
Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an intermediate denoising prediction…
PerceptionDLM: Parallel Region Perception with Multimodal Diffusion Language Models
Yueyi Sun, Yuhao Wang, Jason Li +8
Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks. However, most existing MLLMs rely on autoregressive generation, which limi…
Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
Gongbo Zhang, Wen Wang, Ye Tian +1
Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. Wh…
Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models
Yinjie Wang, Ling Yang, Bowen Li +3
We propose TraceRL, a trajectory-aware reinforcement learning framework for diffusion language models (DLMs) that incorporates preferred inference trajectory into post-training, an…
Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning
Yinjie Wang, Ling Yang, Ye Tian +2
We propose CURE, a novel reinforcement learning framework with a dedicated reward design that co-evolves coding and unit test generation capabilities based on their interaction out…
MMaDA: Multimodal Large Diffusion Language Models
Ling Yang, Ye Tian, Bowen Li +4
We introduce MMaDA, a novel class of multimodal diffusion foundation models designed to achieve superior performance across diverse domains such as textual reasoning, multimodal un…
Training-free Diffusion Acceleration with Bottleneck Sampling
Ye Tian, Xin Xia, Yuxi Ren +6
Diffusion models have demonstrated remarkable capabilities in visual content generation but remain challenging to deploy due to their high computational cost during inference. This…
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening
Ye Tian, Ling Yang, Xinchen Zhang +3
We propose Diffusion-Sharpening, a fine-tuning approach that enhances downstream alignment by optimizing sampling trajectories. Existing RL-based fine-tuning methods focus on singl…
HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation
Ling Yang, Xinchen Zhang, Ye Tian +4
The remarkable success of the autoregressive paradigm has made significant advancement in Multimodal Large Language Models (MLLMs), with powerful models like Show-o, Transfusion an…
VideoTetris: Towards Compositional Text-to-Video Generation
Ye Tian, Ling Yang, Haotian Yang +9
Diffusion models have demonstrated great success in text-to-video (T2V) generation. However, existing methods may face challenges when handling complex (long) video generation scen…
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models
Xinchen Zhang, Ling Yang, Yaqi Cai +8
Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositio…