1 citations · 1 across the 8 of their papers we have counts for
8 papers
Boosting Reinforcement Learning with Verifiable Rewards via Randomly Selected Few-Shot Guidance
Kai Yan, Alexander G. Schwing, Yu-Xiong Wang
Reinforcement Learning with Verifiable Rewards (RLVR) has achieved great success in developing Large Language Models (LLMs) with chain-of-thought rollouts for many tasks such as ma…
Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion
Zhenggang Tang, Yuehao Wang, Yuchen Fan +9
Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate…
Latent Wasserstein Adversarial Imitation Learning
Siqi Yang, Kai Yan, Alexander G. Schwing +1
Imitation Learning (IL) enables agents to mimic expert behavior by learning from demonstrations. However, traditional IL methods require large amounts of medium-to-high-quality dem…
VideoSketcher: Sequential Sketch Generation Using Video Model Priors
Hui Ren, Yuval Alaluf, Omer Bar Tal +3
Sketching is inherently sequential: strokes are drawn progressively to explore and refine ideas. Yet most generative approaches treat sketches as static images, ignoring the tempor…
Variational Masked Diffusion Models
Yichi Zhang, Alex Schwing, Zhizhen Zhao
Masked diffusion models have recently emerged as a flexible framework for discrete generative modeling. However, a key limitation of standard masked diffusion is its inability to e…
Hierarchical Rectified Flow Matching with Mini-Batch Couplings
Yichi Zhang, Yici Yan, Alex Schwing +1
Flow matching has emerged as a compelling generative modeling approach that is widely used across domains. To generate data via a flow matching model, an ordinary differential equa…
DiT-Air: Revisiting the Efficiency of Diffusion Model Architecture Design in Text to Image Generation
Chen Chen, Rui Qian, Wenze Hu +8
In this work, we empirically study Diffusion Transformers (DiTs) for text-to-image generation, focusing on architectural choices, text-conditioning strategies, and training protoco…
Towards Hierarchical Rectified Flow
Yichi Zhang, Yici Yan, Alex Schwing +1
We formulate a hierarchical rectified flow to model data distributions. It hierarchically couples multiple ordinary differential equations (ODEs) and defines a time-differentiable…