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Hao Liang is a Research Associate at King's College London. He received his PhD from The Chinese University of Hong Kong (CUHK), Shenzhen, under the supervision of Zhi-Quan (Tom) Luo. His research lies at the intersection of statistical and computational efficiency in decision-making algorithms, with a particular focus on risk awareness, safety, and multi-agent systems. He is also interested in exploring the applications of these algorithms in various domains, including large language models (LLMs), energy system, and robotics.
Email: haoliang1 at link.cuhk.edu.cn
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News
October 2025: Our paper, "Why GRPO Needs Normalization: A Local-Curvature Perspective on Adaptive Gradients" will be presented at NeurIPS 2025 Workshop on
Efficient Reasoning
👉 TL;DR: We reveal that GRPO’s normalization acts as an adaptive gradient mechanism aligned with local curvature, accelerating and stabilizing LLM reasoning training.
September 2025: Our paper, "Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems" is accepted by NeurIPS 2025 (Spotlight)
👉 TL;DR: We develop GSAC, a causality-aware framework enabling provable scalability and fast cross-domain adaptation in large networked systems.
August 2024: Our paper, "An Economic and Low-carbon Dispatch Algorithm for Microgrids with Electric Vehicles" is awarded the best oral presentation at PSET 2024
July 2024: My paper, "Bridging Distributional and Risk-sensitive Reinforcement Learning with
Provable Regret Bounds" is accepted by JMLR
👉 TL;DR: We bridge distributional and risk-sensitive RL under entropic risk measures, achieving near-optimal regret with computationally efficient DRL algorithms.
July 2024: Present "Bridging Distributional and Risk-Sensitive Reinforcement Learning: Balancing Statistical,
Computational, and Risk Considerations" at ICML 2024 FoRLaC Workshop
March 2024: Deliver a talk "Efficient Risk-aware Decision-making: A Distributional Perspective" at Vector Institute
March 2024: Deliver a talk "A Distribution Optimization Framework for Confidence Bounds of Risk
Measures" at the Informs Optimization Society (IOS) Conference
Janurary 2024: My paper, "Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz
Dynamic Risk Measures" is accepted at AISTATS 2024
July 2023: Present "A Distribution Optimization Framework for Confidence Bounds of Risk
Measures" at ICML 2023
I am serving as Local Organizing Chair of the 7th International Conference on Distributed Artificial Intelligence (DAI 2025), which will be held on November 21–24, 2025 in London, UK.
The conference will bring together researchers and practitioners to discuss the latest advances in distributed AI and multi-agent systems.
We warmly welcome the community to join us in London!
Selected Papers
| Why GRPO Needs Normalization: A Local-Curvature Perspective on Adaptive Gradients |
NeurIPS 2025 Workshop on Efficient Reasoning |
| Cheng Ge*, Heqi Yin*, Hao Liang†, Jiawei Zhang†|
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| Causality Meets Locality: Provably Generalizable and Scalable Policy Learning for Networked Systems |
NeurIPS 2025 (Spotlight) |
| Hao Liang*, Shuqing Shi*, Yudi Zhang, Biwei Huang, Yali Du |
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| Bridging Distributional and Risk-sensitive Reinforcement Learning with
Provable Regret Bounds [link] |
JMLR |
| Hao Liang, Zhi-Quan Luo |
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| Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz
Dynamic Risk Measures [link] |
AISTATS 2024 |
| Hao Liang, Zhi-Quan Luo |
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| A Distribution Optimization Framework for Confidence Bounds of Risk
Measures [link] |
ICML 2023 |
| Hao Liang, Zhi-Quan Luo |
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