8 papers
LAPS: A Length-Aware-Prefill LLM Serving System
Jianshu She, Zonghang Li, Hongchao Du +7
LAPS identifies and disaggregates requests with different prompt lengths in LLM serving to reduce TTFT latency. While recent systems have decoupled the prefill and decode stages to…
Vision-G1: Towards General Vision Language Reasoning with Multi-Domain Data Curation
Yuheng Zha, Kun Zhou, Yujia Wu +7
Despite their success, current training pipelines for reasoning VLMs focus on a limited range of tasks, such as mathematical and logical reasoning. As a result, these models face d…
Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models
Yuan Li, Zhengzhong Liu, Eric Xing
Optimizing data mixtures for supervised fine-tuning (SFT) of large language models (LLMs) is critical for developing general-purpose models, yet this area remains underexplored. In…
How Does Controllability Emerge In Language Models During Pretraining?
Jianshu She, Xinyue Li, Eric Xing +2
Language models can be steered by modifying their internal representations to control concepts such as emotion, style, or truthfulness in generation. However, the conditions for an…
Token Level Routing Inference System for Edge Devices
Jianshu She, Wenhao Zheng, Zhengzhong Liu +4
The computational complexity of large language model (LLM) inference significantly constrains their deployment efficiency on edge devices. In contrast, small language models offer…
MegaMath: Pushing the Limits of Open Math Corpora
Fan Zhou, Zengzhi Wang, Nikhil Ranjan +5
Mathematical reasoning is a cornerstone of human intelligence and a key benchmark for advanced capabilities in large language models (LLMs). However, the research community still l…