3 citations · 3 across the 7 of their papers we have counts for
8 papers
Expertise need not monopolize: Action-Specialized Mixture of Experts for Vision-Language-Action Learning
Weijie Shen, Yitian Liu, Yuhao Wu +10
Vision-Language-Action (VLA) models are experiencing rapid development and demonstrating promising capabilities in robotic manipulation tasks. However, scaling up VLA models presen…
Vlaser: Vision-Language-Action Model with Synergistic Embodied Reasoning
Ganlin Yang, Tianyi Zhang, Haoran Hao +15
While significant research has focused on developing embodied reasoning capabilities using Vision-Language Models (VLMs) or integrating advanced VLMs into Vision-Language-Action (V…
SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning
Haozhan Li, Yuxin Zuo, Jiale Yu +18
Vision-Language-Action (VLA) models have recently emerged as a powerful paradigm for robotic manipulation. Despite substantial progress enabled by large-scale pretraining and super…
EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence
Xinjie Wang, Liu Liu, Yu Cao +5
Constructing a physically realistic and accurately scaled simulated 3D world is crucial for the training and evaluation of embodied intelligence tasks. The diversity, realism, low…
TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
Yangguang Li, Zi-Xin Zou, Zexiang Liu +8
Recent advancements in diffusion techniques have propelled image and video generation to unprecedented levels of quality, significantly accelerating the deployment and application…
CondiQuant: Condition Number Based Low-Bit Quantization for Image Super-Resolution
Kai Liu, Dehui Wang, Zhiteng Li +5
Low-bit model quantization for image super-resolution (SR) is a longstanding task that is renowned for its surprising compression and acceleration ability. However, accuracy degrad…
The Super Weight in Large Language Models
Mengxia Yu, De Wang, Qi Shan +2
Recent works have shown a surprising result: a small fraction of Large Language Model (LLM) parameter outliers are disproportionately important to the quality of the model. LLMs co…
On Inductive Biases That Enable Generalization of Diffusion Transformers
Jie An, De Wang, Pengsheng Guo +2
Recent work studying the generalization of diffusion models with UNet-based denoisers reveals inductive biases that can be expressed via geometry-adaptive harmonic bases. However,…