9 citations · 10 across the 11 of their papers we have counts for
7 papers · 1 filter
Affix Cache for Diffusion Large Language Models
Kaihua Liang, An Zhong, Xin Tan +4
Diffusion Large Language Models (DLLMs) enable non-autoregressive decoding, but efficient inference support remains immature: unlike autoregressive models, whose requests reuse a s…
Frontier: Towards Comprehensive and Accurate LLM Inference Simulation
Yicheng Feng, Xin Tan, Yangtao Deng +3
Modern LLM serving is no longer homogeneous or monolithic. Production systems now combine disaggregated execution, complex parallelism, runtime optimizations, and stateful workload…
Libra: Efficient Resource Management for Agentic RL Post-Training
Kaiwen Chen, Xin Tan, Jingzong Li +6
Reinforcement learning (RL) has emerged as a standard post-training paradigm for shaping large language models (LLMs) into capable agents. In agentic RL, the rollout stage generate…
Continual-NExT: A Unified Comprehension And Generation Continual Learning Framework
Jingyang Qiao, Zhizhong Zhang, Xin Tan +3
Dual-to-Dual MLLMs refer to Multimodal Large Language Models, which can enable unified multimodal comprehension and generation through text and image modalities. Although exhibitin…
Decoupled Reasoning with Implicit Fact Tokens (DRIFT): A Dual-Model Framework for Efficient Long-Context Inference
Wenxuan Xie, Yujia Wang, Xin Tan +3
The integration of extensive, dynamic knowledge into Large Language Models (LLMs) remains a significant challenge due to the inherent entanglement of factual data and reasoning pat…
FOCUS: DLLMs Know How to Tame Their Compute Bound
Kaihua Liang, Xin Tan, An Zhong +2
Diffusion Large Language Models (DLLMs) offer a compelling alternative to Auto-Regressive models, but their deployment is constrained by high decoding cost. In this work, we identi…
OrchestrRL: Dynamic Compute and Network Orchestration for Disaggregated RL
Xin Tan, Yicheng Feng, Yu Zhou +3
Disaggregating the generation and training stages in RL is widely adopted to scale LLM post-training. There are two critical challenges here. First, the generation stage often beco…