12 citations · 23 across the 19 of their papers we have counts for
23 papers
ReFlow: Replay-Driven Decoupling for Efficient RL Post-Training
Yukun Tian, Junwei Su, Haofeng Xu +6
Reinforcement learning (RL) has been a critical paradigm for improving large language model (LLM) reasoning and agentic capabilities, but its efficiency remains a major bottleneck.…
Reward-Aligned Reweighting for On-Policy Distillation
Haofeng Xu, Junwei Su, Lansong Diao +2
On-policy distillation (OPD) trains a student language model with dense feedback from a stronger teacher on student-generated trajectories. Yet standard OPD weights token-level dis…
Structuring Relations Among Learning Paradigms via Protocol--Objective--Resource Reductions
Junwei Su, Changjie Wang, Dongyang Chang
Modern machine learning spans supervised, transfer, continual, meta-learning, and related regimes that often reuse the same hypothesis classes, architectures, and optimizers but di…
Structuring Semantic Embeddings for Principle Evaluation: A Prototype-Guided Contrastive Learning Approach
Che Shen, Junwei Su, Lingpeng Kong +1
Reliable post-hoc evaluation asks whether already generated text satisfies a target criterion after generation. In this paper we study a focused frozen-embedding setting using prin…
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…
When Do Multi-Agent Systems Outperform? Analysing the Learning Efficiency of Agentic Systems
Junwei Su, Chuan Wu
Reinforcement Learning (RL) has emerged as a crucial method for training or fine-tuning large language models (LLMs), enabling adaptive, task-specific optimizations through interac…
ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation
Jingqi Zhou, Sheng Wang, Dezhao Deng +9
LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution. Such rigidity forces a trade-off between do…
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…
The Role of Social Support and Influencers in Social Media Communities
Junwei Su, Peter Marbach
How can individual agents coordinate their actions to achieve a shared objective in distributed systems? This challenge spans economic, technical, and sociological domains, each co…
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…
Towards Robust Graph Incremental Learning on Evolving Graphs
Junwei Su, Difan Zou, Zijun Zhang +1
Incremental learning is a machine learning approach that involves training a model on a sequence of tasks, rather than all tasks at once. This ability to learn incrementally from a…
MTRGL:Effective Temporal Correlation Discerning through Multi-modal Temporal Relational Graph Learning
Junwei Su, Shan Wu, Jinhui Li
In this study, we explore the synergy of deep learning and financial market applications, focusing on pair trading. This market-neutral strategy is integral to quantitative finance…
MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline
Guangming Sheng, Junwei Su, Chao Huang +1
Memory-based Temporal Graph Neural Networks (MTGNNs) are a class of temporal graph neural networks that utilize a node memory module to capture and retain long-term temporal depend…
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…
On the Limitation and Experience Replay for GNNs in Continual Learning
Junwei Su, Difan Zou, Chuan Wu
Continual learning seeks to empower models to progressively acquire information from a sequence of tasks. This approach is crucial for many real-world systems, which are dynamic an…
ABC: Aggregation before Communication, a Communication Reduction Framework for Distributed Graph Neural Network Training and Effective Partition
Junwei Su
Graph Neural Networks(GNNs) are a family of neural models tailored for graph-structure data and have shown superior performance in learning representations for graph-structured dat…
Structure of Core-Periphery Communities
Junwei Su, Peter Marbach
It has been experimentally shown that communities in social networks tend to have a core-periphery topology. However, there is still a limited understanding of the precise structur…