1 citations · 2 across the 5 of their papers we have counts for
7 papers
Human Demonstrations as Post-Training Data for Any Robot Body: Retargeting the Object, Not the Hand
Nagasrikanth Kallakuri
Human demonstrations are today used largely for pretraining vision-language-action models, with little ability to use the same data for target-embodiment or task-oriented training,…
HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models
Renjie Xie, Juncheng Yang, Aoting Hu +4
Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughput. This bottleneck remains…
AFD-Ledger: Deployment Provisioning for Attention--FFN Disaggregation
Chengyu Qiu, Xiao Fu, Fengcun Li +6
Attention--Feed-Forward Network (FFN) Disaggregation (AFD) is emerging as a promising architecture for serving Mixture-of-Experts (MoE) language models. While existing AFD systems…
From Prefix Cache to Fusion RAG Cache: Accelerating LLM Inference in Retrieval-Augmented Generation
Jiahao Wang, Weiyu Xie, Mingxing Zhang +10
Retrieval-Augmented Generation enhances Large Language Models by integrating external knowledge, which reduces hallucinations but increases prompt length. This increase leads to hi…
Seer: Online Context Learning for Fast Synchronous LLM Reinforcement Learning
Ruoyu Qin, Weiran He, Weixiao Huang +7
Reinforcement Learning (RL) has emerged as a critical technique for advancing modern Large Language Models (LLMs), yet existing synchronous RL systems face severe performance bottl…
RetroInfer: A Vector Storage Engine for Scalable Long-Context LLM Inference
Yaoqi Chen, Jinkai Zhang, Baotong Lu +16
Recent large language models (LLMs) are rapidly extending their context windows, yet inference throughput lags due to increasing GPU memory and bandwidth demands. This is because t…
MoBA: Mixture of Block Attention for Long-Context LLMs
Enzhe Lu, Zhejun Jiang, Jingyuan Liu +22
Scaling the effective context length is essential for advancing large language models (LLMs) toward artificial general intelligence (AGI). However, the quadratic increase in comput…