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Showing papers from Huawei Technologies Co., Ltd. Show all papers

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NeurIPS 2026Huazhong AgriculturalHuawei Technologies Co., Ltd.U BristolNational University of Defense TWuhan TextileMulti-agent RL

From Local to Global: Progressive Consensus via Hierarchical Communication in Multi-Agent Reinforcement Learning

Jiangjin Yin, Zhonglin Lv, Hangyu Mao, Rongbo Zhu and 4 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Huazhong AgriculturalHuawei Technologies Co., Ltd.School of Artificial IntelligencMulti-agent RL

Semantic-level Exploration for Multi-Agent Reinforcement Learning

Jiangjin Yin, Zijian Ye, Rongbo Zhu, Hangyu Mao and 1 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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57%Worth a look
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NeurIPS 2026Huawei Technologies Co., Ltd.Huawei Tech. Investment Co., LimHuawei Technologies Ltd.Alignment & preference optimization

The Post-Training Dilemma: Why We Should Rethink the Sequential SFT-RL Paradigm

Xueyan Niu, Bo Bai, Wei Han, Weixi Zhang

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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80%Must read
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NeurIPS 2026The Hong Kong University of ScieThe Chinese University of Hong KKuaishouCornellHuazhong University of Science aDeep RL

GARDO: Reinforcing Diffusion Models without Reward Hacking

GARDO selectively regularizes high-uncertainty diffusion samples and adaptively updates reference models to prevent reward hacking while preserving diversity and sample efficiency.

Haoran He, Yuxiao YE, Jie Liu, Jiajun Liang and 7 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 30 on Hugging Face · Code ★ 63

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AI panel: 12 of 20 reviewers recommend it
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medium 7/10
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