5 citations · 9 across the 17 of their papers we have counts for
11 papers · 1 filter
LOCAL: Enabling Learning On-device Contiguously for Agent LLMs
Xinxin Liu, Jiaxin Li, Zibo Wang +7
On-device LLM agents interact repeatedly with users on local hardware, producing private traces that are valuable for adaptation but should not be sent to a remote trainer. Ideally…
Scheduling Mixed RL Rollouts Beyond Prefix Locality
Zetao Hong, Song Yuan, Yuanhao Ding +4
Modern reinforcement learning (RL) post-training pipelines for large language models (LLMs) increasingly combine rollout workloads across multiple domains and feedback paradigms. P…
TIDE-MC: Two-Sided Interpolative Decomposition for Billion-Scale GPU Matrix Completion
Chengying Huan, Yubo Wang, Pinhuan Wang +11
Matrix completion supports large-scale recommendation and scientific computing, yet existing GPU solvers commonly assume that the observed matrix or its dense factors fit in device…
STAR: Decode-Phase Rescheduling for LLM Inference
Zhibin Wang, Zetao Hong, Xue Li +8
Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly f…
SmartSwap: Swap-Based Memory Optimization for LLM Training under Varying Operator Sequences
Zibo Wang, Yuhang Zhou, Zhibin Wang +13
The increasing size of large language models (LLMs) has led to a surge in memory requirements during training, often exceeding the capacity of high-bandwidth memory (HBM). Swap-bas…
Accelerating Mixture-of-Experts Inference by Hiding Offloading Latency with Speculative Decoding
Zhibin Wang, Zhonghui Zhang, Yuhang Zhou +8
Recent advancements in Mixture of Experts (MoE) models have significantly increased their parameter scale as well as model performance. Extensive offloading techniques have been pr…