110 citations · 285 across the 58 of their papers we have counts for
10 papers · 1 filter
SPID: Distilled Protein Backbone Generation
Liyang Xie, Haoran Zhang, Zhendong Wang +2
Diffusion- and flow-based generative models have recently demonstrated strong performance in protein backbone generation tasks, offering unprecedented capabilities for de novo prot…
Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout Replay
Yifan Sun, Jingyan Shen, Yibin Wang +4
Reinforcement learning (RL) has become an effective approach for fine-tuning large language models (LLMs), particularly to enhance their reasoning capabilities. However, RL fine-tu…
Score Distillation Beyond Acceleration: Generative Modeling from Corrupted Data
Yasi Zhang, Tianyu Chen, Zhendong Wang +3
Learning generative models directly from corrupted observations is a long standing challenge across natural and scientific domains. We introduce Restoration Score Distillation (RSD…
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation
Tianyu Chen, Yasi Zhang, Zhendong Wang +3
Diffusion models have achieved remarkable success in generating high-resolution, realistic images across diverse natural distributions. However, their performance heavily relies on…
Score identity Distillation: Exponentially Fast Distillation of Pretrained Diffusion Models for One-Step Generation
Mingyuan Zhou, Huangjie Zheng, Zhendong Wang +2
We introduce Score identity Distillation (SiD), an innovative data-free method that distills the generative capabilities of pretrained diffusion models into a single-step generator…
Diffusion Policies creating a Trust Region for Offline Reinforcement Learning
Tianyu Chen, Zhendong Wang, Mingyuan Zhou
Offline reinforcement learning (RL) leverages pre-collected datasets to train optimal policies. Diffusion Q-Learning (DQL), introducing diffusion models as a powerful and expressiv…
Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization
Xuxi Chen, Zhendong Wang, Daouda Sow +5
In the rapidly advancing arena of large language models (LLMs), a key challenge is to enhance their capabilities amid a looming shortage of high-quality training data. Our study st…
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou
Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is an important paradigm of RL. Standard RL methods often pe…
Diffusion-GAN: Training GANs with Diffusion
Zhendong Wang, Huangjie Zheng, Pengcheng He +2
Generative adversarial networks (GANs) are challenging to train stably, and a promising remedy of injecting instance noise into the discriminator input has not been very effective…
Implicit Distributional Reinforcement Learning
Yuguang Yue, Zhendong Wang, Mingyuan Zhou
To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributiona…