1 citations · 1 across the 7 of their papers we have counts for
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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…
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…
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…