7 papers · 1 filter
One step further with Monte-Carlo sampler to guide diffusion better
Minsi Ren, Wenhao Deng, Ruiqi Feng +1
Stochastic differential equation (SDE)-based generative models have achieved substantial progress in conditional generation via training-free differentiable loss-guided approaches.…
GenCP: Towards Generative Modeling Paradigm of Coupled Physics
Tianrun Gao, Haoren Zheng, Wenhao Deng +5
Real-world physical systems are inherently complex, often involving the coupling of multiple physics, making their simulation both highly valuable and challenging. Many mainstream…
Unlocking Reasoning Capabilities in LLMs via Reinforcement Learning Exploration
Wenhao Deng, Long Wei, Chenglei Yu +1
Reinforcement learning with verifiable rewards (RLVR) has recently enhanced the reasoning capabilities of large language models (LLMs), particularly for mathematical problem solvin…
On the Guidance of Flow Matching
Ruiqi Feng, Chenglei Yu, Wenhao Deng +2
Flow matching has shown state-of-the-art performance in various generative tasks, ranging from image generation to decision-making, where generation under energy guidance (abbrevia…
VFScale: Intrinsic Reasoning through Verifier-Free Test-time Scalable Diffusion Model
Tao Zhang, Jia-Shu Pan, Ruiqi Feng +1
Inspired by human SYSTEM 2 thinking, LLMs excel at complex reasoning tasks via extended Chain-of-Thought. However, similar test-time scaling for diffusion models to tackle complex…
Wavelet Diffusion Neural Operator
Peiyan Hu, Rui Wang, Xiang Zheng +7
Simulating and controlling physical systems described by partial differential equations (PDEs) are crucial tasks across science and engineering. Recently, diffusion generative mode…