activity
20112024
most citedAssessing and Understanding Creativity in Large Language Models

35 citations · 61 across the 18 of their papers we have counts for

collaborators

18 papers

cs.CV2024

Prompt-based Visual Alignment for Zero-shot Policy Transfer

Haihan Gao, Rui Zhang, Qi Yi +13

Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature ex…

cs.AI2024

Luban: Building Open-Ended Creative Agents via Autonomous Embodied Verification

Yuxuan Guo, Shaohui Peng, Jiaming Guo +15

Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined…

cs.CL2024★ 35 cited

Assessing and Understanding Creativity in Large Language Models

Yunpu Zhao, Rui Zhang, Wenyi Li +10

In the field of natural language processing, the rapid development of large language model (LLM) has attracted more and more attention. LLMs have shown a high level of creativity i…

cs.LG2023★ 1 cited

Context Shift Reduction for Offline Meta-Reinforcement Learning

Yunkai Gao, Rui Zhang, Jiaming Guo +10

Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks. However, the context shift problem…

cs.LG2023★ 1 cited

Efficient Symbolic Policy Learning with Differentiable Symbolic Expression

Jiaming Guo, Rui Zhang, Shaohui Peng +8

Deep reinforcement learning (DRL) has led to a wide range of advances in sequential decision-making tasks. However, the complexity of neural network policies makes it difficult to…

cs.LG2023★ 1 cited

Contrastive Modules with Temporal Attention for Multi-Task Reinforcement Learning

Siming Lan, Rui Zhang, Qi Yi +10

In the field of multi-task reinforcement learning, the modular principle, which involves specializing functionalities into different modules and combining them appropriately, has b…