7 citations · 8 across the 12 of their papers we have counts for
13 papers
Scaling Laws for Collapse in Asynchronous GRPO
Jingwei Song, Haofeng Xu, Jie Xiao +10
Asynchronous reinforcement learning improves the throughput of large language model post-training by decoupling rollout generation from policy optimization, but introduces a mismat…
Step-TP: A Grounded, Step-Level Dataset with Chain-of-Thought Reasoning for LLM-Guided Tensor Program Optimization
Mengfan Liu, Da Zheng, Junwei Su +1
Despite the strong reasoning capabilities of large language models (LLMs), optimizing the execution efficiency of tensor programs remains challenging due to the need for precise, c…
GeomHerd: A Forward-looking Herding Quantification via Ricci Flow Geometry on Agent Interactive Simulations
Lake Yang, Junwei Su, Jingfeng Zeng +5
Herding -- where agents align their behaviors and act collectively -- is a central driver of market fragility and systemic risk. Existing approaches to quantify herding rely on pri…
GAC: Stabilizing Asynchronous RL Training for LLMs via Gradient Alignment Control
Haofeng Xu, Junwei Su, Yukun Tian +3
Asynchronous execution is essential for scaling reinforcement learning (RL) to modern large model workloads, including large language models and AI agents, but it can fundamentally…
Full-Graph vs. Mini-Batch Training: Comprehensive Analysis from a Batch Size and Fan-Out Size Perspective
Mengfan Liu, Da Zheng, Junwei Su +1
Full-graph and mini-batch Graph Neural Network (GNN) training approaches have distinct system design demands, making it crucial to choose the appropriate approach to develop. A cor…
A Non-Asymptotic Convergent Analysis for Scored-Based Graph Generative Model via a System of Stochastic Differential Equations
Junwei Su, Chuan Wu
Score-based graph generative models (SGGMs) have proven effective in critical applications such as drug discovery and protein synthesis. However, their theoretical behavior, partic…
On the Interplay between Graph Structure and Learning Algorithms in Graph Neural Networks
Junwei Su, Chuan Wu
This paper studies the interplay between learning algorithms and graph structure for graph neural networks (GNNs). Existing theoretical studies on the learning dynamics of GNNs pri…
Heta: Distributed Training of Heterogeneous Graph Neural Networks
Yuchen Zhong, Junwei Su, Chuan Wu +1
Heterogeneous Graph Neural Networks (HGNNs) leverage diverse semantic relationships in Heterogeneous Graphs (HetGs) and have demonstrated remarkable learning performance in various…
Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems
Junwei Su, Difan Zou, Chuan Wu
Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice and plays an important role in the generalization of modern machine learning. Howev…
BG-HGNN: Toward Efficient Learning for Complex Heterogeneous Graphs
Junwei Su, Lingjun Mao, Zheng Da +1
Heterogeneous graphs, comprising diverse node and edge types connected through varied relations, are ubiquitous in real-world applications. Message-passing heterogeneous graph neur…
On the Topology Awareness and Generalization Performance of Graph Neural Networks
Junwei Su, Chuan Wu
Many computer vision and machine learning problems are modelled as learning tasks on graphs where graph neural networks GNNs have emerged as a dominant tool for learning representa…
PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks
Junwei Su, Difan Zou, Chuan Wu
Memory-based Dynamic Graph Neural Networks (MDGNNs) are a family of dynamic graph neural networks that leverage a memory module to extract, distill, and memorize long-term temporal…
CDMPP: A Device-Model Agnostic Framework for Latency Prediction of Tensor Programs
Hanpeng Hu, Junwei Su, Juntao Zhao +4
Deep Neural Networks (DNNs) have shown excellent performance in a wide range of machine learning applications. Knowing the latency of running a DNN model or tensor program on a spe…