1 citations · 1 across the 7 of their papers we have counts for
9 papers
Asynchronous LLM Post-Training: Group-Mass Capping and Convergence Analysis
Qijia He, Ruinan Jin, Jun Luo +2
Asynchronous reinforcement learning (RL) improves the efficiency of large language model post-training but introduces stale rollouts generated by earlier policies. Theoretical unde…
Adam under Generalized Smoothness with Second-Moment-Type Stochastic Gradients
Ruinan Jin, Difei Cheng, Ling Chen +3
Adam is widely observed to remain stable even when the objective deviates significantly from global smoothness. Under the generalized smoothness framework, however, existing analys…
Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails
Ruinan Jin, Yingbin Liang, Shaofeng Zou
Despite Adam demonstrating faster empirical convergence than SGD in many applications, much of the existing theory yields guarantees essentially comparable to those of SGD, leaving…
TIC-GRPO: Provable and Efficient Optimization for Reinforcement Learning from Human Feedback
Lei Pang, Jun Luo, Ruinan Jin
Group Relative Policy Optimization (GRPO) trains language models without a value critic using rewards centered within sampled response groups. We study how importance weighting, cl…
NaviAgent: Graph-Driven Bilevel Planning for Scalable Tool Orchestration
Yan Jiang, Hao Zhou, Lizhong GU +4
Large Language Models (LLMs) increasingly act as function-call agents that invoke external tools to tackle tasks beyond their static knowledge. However, they typically invoke tools…
Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum
Difei Cheng, Ruinan Jin, Hong Qiao +1
Distributed stochastic gradient methods are widely used to preserve data privacy and ensure scalability in large-scale learning tasks. While existing theory on distributed momentum…
Stochastic Gradient Descent in Non-Convex Problems: Asymptotic Convergence with Relaxed Step-Size via Stopping Time Methods
Ruinan Jin, Difei Cheng, Hong Qiao +3
Stochastic Gradient Descent (SGD) is widely used in machine learning research. Previous convergence analyses of SGD under the vanishing step-size setting typically require Robbins-…
Careful Seeding for k-Medois Clustering with Incremental k-Means++ Initialization
Difei Cheng, Yunfeng Zhang, Ruinan Jin
K-medoids clustering is a popular variant of k-means clustering and widely used in pattern recognition and machine learning. A main drawback of k-medoids clustering is that an impr…
Fast Density Estimation for Density-based Clustering Methods
Difei Cheng, Ruihang Xu, Bo Zhang +1
Density-based clustering algorithms are widely used for discovering clusters in pattern recognition and machine learning since they can deal with non-hyperspherical clusters and ar…