86 citations · 154 across the 84 of their papers we have counts for
8 papers · 1 filter
Solving the Granularity Mismatch: Hierarchical Preference Learning for Long-Horizon LLM Agents
Heyang Gao, Zexu Sun, Erxue Min +4
Large Language Models (LLMs) as autonomous agents are increasingly tasked with solving complex, long-horizon problems. Aligning these agents via preference-based offline methods li…
CurES: From Gradient Analysis to Efficient Curriculum Learning for Reasoning LLMs
Yongcheng Zeng, Zexu Sun, Bokai Ji +7
Curriculum learning plays a crucial role in enhancing the training efficiency of large language models (LLMs) on reasoning tasks. However, existing methods often fail to adequately…
Staying in the Sweet Spot: Responsive Reasoning Evolution via Capability-Adaptive Hint Scaffolding
Ziheng Li, Zexu Sun, Jinman Zhao +8
Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in enhancing the reasoning capabilities of large language models (LLMs). However, existing RLV…
Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation
Dongsheng Zhu, Weixian Shi, Zhengliang Shi +4
Although current Large Language Models (LLMs) exhibit impressive capabilities, performing complex real-world tasks still requires tool learning. Mainstream methods, such as CoT/ReA…
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)
Yuchen Li, Haoyi Xiong, Linghe Kong +4
Both Transformer and Graph Neural Networks (GNNs) have been employed in the domain of learning to rank (LTR). However, these approaches adhere to two distinct yet complementary pro…
Variational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Yige Zhao, Jianxiang Yu, Yao Cheng +4
Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existi…
HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness
Zeyuan Zhao, Qingqing Ge, Anfeng Cheng +3
Heterogeneous graph neural networks(HGNNs) have recently shown impressive capability in modeling heterogeneous graphs that are ubiquitous in real-world applications. Most existing…
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
Qingqing Ge, Zeyuan Zhao, Yiding Liu +4
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to vario…