111 citations · 229 across the 16 of their papers we have counts for
17 papers
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning
Siyan Zhao, Devaansh Gupta, Qinqing Zheng +1
Recent large language models (LLMs) have demonstrated strong reasoning capabilities that benefits from online reinforcement learning (RL). These capabilities have primarily been de…
Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
Qinqing Zheng, Mikael Henaff, Amy Zhang +2
Automatically synthesizing dense rewards from natural language descriptions is a promising paradigm in reinforcement learning (RL), with applications to sparse reward problems, ope…
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning
Zihan Ding, Amy Zhang, Yuandong Tian +1
We introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently. As opposed to traditional one-step d…
Guided Flows for Generative Modeling and Decision Making
Qinqing Zheng, Matt Le, Neta Shaul +3
Classifier-free guidance is a key component for enhancing the performance of conditional generative models across diverse tasks. While it has previously demonstrated remarkable imp…
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang +1
The goal of reinforcement learning (RL) is to find a policy that maximizes the expected cumulative return. It has been shown that this objective can be represented as an optimizati…
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Qinqing Zheng, Mikael Henaff, Brandon Amos +1
Natural agents can effectively learn from multiple data sources that differ in size, quality, and types of measurements. We study this heterogeneity in the context of offline reinf…
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning
Tung Nguyen, Qinqing Zheng, Aditya Grover
Behavioral cloning (BC) provides a straightforward solution to offline RL by mimicking offline trajectories via supervised learning. Recent advances (Chen et al., 2021; Janner et a…
Latent State Marginalization as a Low-cost Approach for Improving Exploration
Dinghuai Zhang, Aaron Courville, Yoshua Bengio +3
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilisti…
Online Decision Transformer
Qinqing Zheng, Amy Zhang, Aditya Grover
Recent work has shown that offline reinforcement learning (RL) can be formulated as a sequence modeling problem (Chen et al., 2021; Janner et al., 2021) and solved via approaches s…
Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?
Shuxiao Chen, Qinqing Zheng, Qi Long +1
A widely recognized difficulty in federated learning arises from the statistical heterogeneity among clients: local datasets often originate from distinct yet not entirely unrelate…
Federated -Differential Privacy
Qinqing Zheng, Shuxiao Chen, Qi Long +1
Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their lo…
Near-Optimal Confidence Sequences for Bounded Random Variables
Arun Kumar Kuchibhotla, Qinqing Zheng
Many inference problems, such as sequential decision problems like A/B testing, adaptive sampling schemes like bandit selection, are often online in nature. The fundamental problem…
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion
Qinqing Zheng, Jinshuo Dong, Qi Long +1
Datasets containing sensitive information are often sequentially analyzed by many algorithms. This raises a fundamental question in differential privacy regarding how the overall p…
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Qinqing Zheng, Bor-Yiing Su, Jiyan Yang +7
Recommendation systems are often trained with a tremendous amount of data, and distributed training is the workhorse to shorten the training time. While the training throughput can…
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent
Qinqing Zheng, John Lafferty
We address the rectangular matrix completion problem by lifting the unknown matrix to a positive semidefinite matrix in higher dimension, and optimizing a nonconvex objective over…
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
Qinqing Zheng, John Lafferty
We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite…
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Qinqing Zheng, Ryota Tomioka
We consider the problem of recovering a low-rank tensor from its noisy observation. Previous work has shown a recovery guarantee with signal to noise ratio $O(n^{\lceil K/2 \rceil…