2 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
Generative Universal Verifier as Multimodal Meta-Reasoner
Xinchen Zhang, Xiaoying Zhang, Youbin Wu +5
We introduce Generative Universal Verifier, a novel concept and plugin designed for next-generation multimodal reasoning in vision-language models and unified multimodal models, pr…
PeRL: Permutation-Enhanced Reinforcement Learning for Interleaved Vision-Language Reasoning
Yizhen Zhang, Yang Ding, Shuoshuo Zhang +9
Inspired by the impressive reasoning capabilities demonstrated by reinforcement learning approaches like DeepSeek-R1, recent emerging research has begun exploring the use of reinfo…
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…
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…