Qinqing Zheng
Member of Technical Staff, Inception
zhengqinqing (at) gmail (dot) com
About
- I am a ML researcher. Currently, I work on diffusion language models and reinforcement learning at Inception.
- Previously, I was on the Llama research team, specializing in post-training for reasoning. Within that effort, I spearheaded the development of the practical domain (non-math/non-coding) expert model. Before that, I worked at FAIR on a combination of RL, diffusion, and LLM reasoning.
- I received my Ph.D.from UChicago, advised by
Prof. John Lafferty (a truly fortunate
experience!). My doctoral research combined theory and practice,
with a focus on exact solutions for nonconvex optimization
problems.
Earlier, I was a research scientist at Facebook, where I helped build the distributed training system supporting Ads ranking. I also worked as a postdoc researcher in Wharton Statistics, focusing on differential privacy and statistical inference.
Papers
-
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning
Siyan Zhao*, Devaansh Gupta*, Qinqing Zheng†, Aditya Grover†
[paper] [code] -
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
DiJia Su, Hanlin Zhu*, Yingchen Xu*, Jiantao Jiao, Yuandong Tian†, Qinqing Zheng†
ICML 2025
[paper] -
Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
Qinqing Zheng*, Mikael Henaff*, Amy Zhang, Aditya Grover, Brandon Amos
RLC 2025
[paper] [code] -
Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces
DiJia Su, Sainaa Sukhbaatar, Michael Rabbat, Yuandong Tian, Qinqing Zheng
ICLR 2025
[paper] [code] -
Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping
Lucas Lehnert, Sainaa Sukhbaatar, DiJia Su, Qinqing Zheng, Paul Mcvay, Michael Rabbat, Yuandong Tian
COLM 2024
[paper] [code] -
Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning
Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng
ICLR 2024 Generative Models for Decision Making Workshop
[paper] -
Guided Flows for Generative Modeling and Decision Making
Qinqing Zheng, Matt Le, Neta Shaul, Yaron Lipman, Aditya Grover, Ricky T. Q. Chen
[paper] -
Dual RL: Unification and New Methods for Reinforcement and Imitation Learning
Harshit Sikchi, Qinqing Zheng, Amy Zhang, Scott Niekum
ICLR 2024 (Spotlight)
[paper] [code] -
Semi-Supervised Offline Reinforcement Learning with Action-Free Trajectories
Qinqing Zheng, Mikael Henaff, Brandon Amos, Aditya Grover
ICML 2023
[paper] [code] -
ConserWeightive Behavioral Cloning for Reliable Offline Reinforcement Learning
Tung Nguyen, Qinqing Zheng, Aditya Grover
NeurIPS 2022 Foundation Models for Decision Making Workshop
[paper] [code] -
Latent State Marginalization as a Low-cost Approach for Improving Exploration
Dinghuai Zhang, Aaron Courville, Yoshua Bengio, Qinqing Zheng, Amy Zhang, Ricky T. Q. Chen
ICLR 2023
[paper] -
Online Decision Transformer
Qinqing Zheng, Amy Zhang, Aditya Grover
ICML 2022 (Long Oral Presentation)
[paper] [code] [poster] -
Near-Optimal Confidence Sequences for Bounded Random Variables
Arun Kumar Kuchibhotla*, Qinqing Zheng* (*Equal contribution)
ICML 2021 (Spotlight)
[paper] [code] -
A Theorem of the Alternative for Personalized Federated Learning Shuxiao Chen, Qinqing Zheng, Qi Long, Weijie Su
Submitted.
[paper] -
Federated \(f\)-Differential Privacy Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie Su
AISTATS 2021
[paper] [code] -
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion Qinqing Zheng, Jinshuo Dong, Qi Long, Weijie Su
ICML 2020
[paper] [code] -
ShadowSync: Performing Synchronization in the Background for Highly Scalable Distributed Training
Qinqing Zheng, Bor-Yiing Su, Jiyan Yang, Alisson Azzolini, Qiang Wu, Ou Jin, Shri Karandikar, Hagay Lupesko, Liang Xiong, Eric Zhou
[paper] -
Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent
Qinqing Zheng, John Lafferty
[paper] -
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements
Qinqing Zheng, John Lafferty
NeurIPS 2015
[paper] [poster] -
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm
Qinqing Zheng , Ryota Tomioka
NeurIPS 2015
[paper] [code] [poster]
Talks
- Princeton University, Nov 2022
- UC Berkeley, March 2022
- Minisymposium on Non-Convex Optimization for Low Complexity Models: Theory and Applications, SIAM Optimization Conference, May 2017
- Microsoft Cambridge Research Seminar, UK, March 2017
- SILO Seminar Series, University of Wisconsin Madison, Jan 2017
- Ming Hsieh Institute Series on Mathematical Foundations of Learning from Signals and Data, USC, Dec 2016
Review
- Conference: ICML, NeurIPS, AISTATS, ICLR
- Journal: Journal of Machine Learning Research (Editorial Board Reviewer), IEEE Transactions on Signal Processing, Annals of Statistics