activity
20242026
most citedTowards Understanding Why Data Augmentation Improves Generalization

1 citations · 2 across the 4 of their papers we have counts for

collaborators

7 papers

cs.DC2026

Splitting Prompt Prefill from Response Replay for Context-Parallel Long-Context LLM Post-Training

Yubing Bao, Zhihui Lu, Qiang Duan +3

Training long-context LLM policies with RL requires re-evaluating groups of sampled responses under the updated policy, an update-stage attention workload that differs sharply from…

cs.CL2025

Bridging Draft Policy Misalignment: Group Tree Optimization for Speculative Decoding

Shijing Hu, Jingyang Li, Zhihui Lu +1

Speculative decoding accelerates large language model (LLM) inference by letting a lightweight draft model propose multiple tokens that the target model verifies in parallel. Yet e…

cs.CV2025★ 1 cited

Towards Understanding Why Data Augmentation Improves Generalization

Jingyang Li, Jiachun Pan, Kim-Chuan Toh +1

Data augmentation is a cornerstone technique in deep learning, widely used to improve model generalization. Traditional methods like random cropping and color jittering, as well as…

cs.CL2025

GRIFFIN: Effective Token Alignment for Faster Speculative Decoding

Shijing Hu, Jingyang Li, Xingyu Xie +3

Speculative decoding accelerates inference in large language models (LLMs) by generating multiple draft tokens simultaneously. However, existing methods often struggle with token m…

cs.LG2024

Memory-Efficient 4-bit Preconditioned Stochastic Optimization

Jingyang Li, Kuangyu Ding, Kim-Chuan Toh +1

Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in lar…

cs.LG2024

Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning

Jingyang Li, Jiachun Pan, Vincent Y. F. Tan +2

Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the cont…

cs.LG2024★ 1 cited

LoCo: Low-Bit Communication Adaptor for Large-scale Model Training

Xingyu Xie, Zhijie Lin, Kim-Chuan Toh +1

To efficiently train large-scale models, low-bit gradient communication compresses full-precision gradients on local GPU nodes into low-precision ones for higher gradient synchroni…