1 citations · 1 across the 11 of their papers we have counts for
11 papers
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…
MoE Proxy Models for Low-Cost Failure Reproduction and Diagnosis in LLM RL Post-Training
Yikai Wang, Chuansai Zhou, Yuhang Zhou +10
Reinforcement learning (RL) post-training of large language models (LLMs) is computationally intensive and involves complex system pipelines with substantial debugging overhead. In…
SpecLA: Efficient Speculative Decoding for Linear-Attention Models
Zhibin Wang, Xuying Han, Zhaohua Yang +5
Linear-attention models replace the growing KV cache with recurrent states, but autoregressive decoding still reads, updates, and writes these states one token at a time. Speculati…
SSV: Sparse Speculative Verification for Efficient LLM Inference
Zhibin Wang, Ziyu Zhong, Nuo Shen +3
Speculative decoding and dynamic sparse attention are two complementary approaches for accelerating long-context LLM inference: the former amortizes target-model execution across m…
DART: Diffusion-Inspired Speculative Decoding for Fast LLM Inference
Fuliang Liu, Xue Li, Ketai Zhao +7
Speculative decoding is an effective and lossless approach for accelerating LLM inference. However, existing widely adopted model-based draft designs, such as EAGLE3, improve accur…
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…
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…
Chordless Structure: A Pathway to Simple and Expressive GNNs
Hongxu Pan, Shuxian Hu, Mo Zhou +5
Researchers have proposed various methods of incorporating more structured information into the design of Graph Neural Networks (GNNs) to enhance their expressiveness. However, the…
CoDec: Prefix-Shared Decoding Kernel for LLMs
Zhibin Wang, Rui Ning, Chao Fang +12
Prefix-sharing among multiple prompts presents opportunities to combine the operations of the shared prefix, while attention computation in the decode stage, which becomes a critic…
Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving
Zhibin Wang, Shipeng Li, Xue Li +7
Large language models have been widely deployed in various applications, encompassing both interactive online tasks and batched offline tasks. Given the burstiness and latency sens…
Revisiting Service Level Objectives and System Level Metrics in Large Language Model Serving
Zhibin Wang, Shipeng Li, Yuhang Zhou +7
User experience is a critical factor Large Language Model (LLM) serving systems must consider, where service level objectives (SLOs) considering the experience of individual reques…