1 citations · 1 across the 5 of their papers we have counts for
5 papers
-Bench: Can Large Language Models Engineer the Infrastructure That Powers Them?
Leilei Ding, Shumin Wang, Yuting Huang +10
Large language models (LLMs) now show strong reasoning and coding abilities, raising the prospect that they could assist in developing and optimizing the very infrastructure that p…
When Do Larger Batches Help Scale LLM Reinforcement Learning?
Ziniu Li, Jinbo Wang, Guanhua Huang +3
Larger batches reduce the variance of stochastic gradients per update and are therefore often expected to accelerate training. Yet whether this statistical benefit translates into…
PrefixFlow: Training-Time KV Caching for Schedule-Level Prefix Reuse in LLM RL Training
Pengbo Li, Feiyuan Zhang, Guangming Sheng +8
In reinforcement learning (RL) post-training, one prompt fans out into trajectories, yet standard trainers replay the shared prefix's forward and backward pass times; in lo…
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for Multi-Turn Dialogue
Feiyuan Zhang, Dezhi Zhu, James Ming +6
Retrieval-Augmented Generation (RAG) systems have shown substantial benefits in applications such as question answering and multi-turn dialogue \citep{lewis2020retrieval}. However,…
Unlocking Full Efficiency of Token Filtering in Large Language Model Training
Di Chai, Pengbo Li, Feiyuan Zhang +7
Token filtering has been proposed to enhance the utility of large language models (LLMs) by eliminating inconsequential tokens during training. While usingfewer tokens is expected…