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20232026
most citedImproving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble

3 citations · 7 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.AI2024

Constrained Human-AI Cooperation: An Inclusive Embodied Social Intelligence Challenge

Weihua Du, Qiushi Lyu, Jiaming Shan +8

We introduce Constrained Human-AI Cooperation (CHAIC), an inclusive embodied social intelligence challenge designed to test social perception and cooperation in embodied agents. In…

cs.CV2024

SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge

Andong Wang, Bo Wu, Sunli Chen +5

Learning commonsense reasoning from visual contexts and scenes in real-world is a crucial step toward advanced artificial intelligence. However, existing video reasoning benchmarks…

cs.CV2024★ 2 cited

COMBO: Compositional World Models for Embodied Multi-Agent Cooperation

Hongxin Zhang, Zeyuan Wang, Qiushi Lyu +7

In this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only egocentric views of the world. To effectively pl…

cs.CV2024★ 2 cited

HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments

Qinhong Zhou, Sunli Chen, Yisong Wang +6

Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the ph…

cs.LG2024★ 3 cited

Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble

Shun Zhang, Zhenfang Chen, Sunli Chen +3

Reinforcement Learning from Human Feedback (RLHF) is a widely adopted approach for aligning large language models with human values. However, RLHF relies on a reward model that is…