Guozheng Ma

I’m Guozheng Ma, a PhD student at Nanyang Technological University, advised by Dacheng Tao. Previously, I completed my master’s studies at Tsinghua University and my bachelor’s degree at Hunan University.

My research focuses on deep reinforcement learning: how agents can learn efficiently, scale, and keep adapting in open-ended environments. I study optimization, network sparsity, plasticity, and the interplay between offline knowledge and online experience. More broadly, I’m interested in RL as a way to understand and develop intelligence.

An illustrated portrait of Guozheng Ma

An illustrated portrait of Guozheng Ma

Greatness cannot be planned, so I play with joyful explorations!

Notes

Why sparsify RL networks?

Static sparsity offers a surprisingly simple way to improve scaling in deep RL.

Keeping visual RL able to learn

Data, the critic and the timing of updates all shape an agent's learning capacity.

Learning from a different view

Useful visual diversity depends on both the transformation and the way it enters training.

Selected research

All Publications

NeurIPS 2026

Position paper

Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors

Let deployment evidence guide how online learners use and adapt their offline priors.

Paper

ICML 2026

Spotlight · Top 2.2%

What Makes Value Learning Efficient in Residual Reinforcement Learning?

Helping residual RL critics learn from a pretrained policy and recognize small action corrections.

ICML 2025

Oral · Top 1%

Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning

Randomly pruning a network before training improves how deep RL scales.