Imagen de Portada

RAST-MoE-RL: A Regime-Aware Spatio-Temporal MoE Framework for Deep Reinforcement Learning in Ride-Hailing

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Tang, Yuhan, Cui, Kangxin, Park, Jung Ho, Zhao, Yibo, Jiang, Xuan, He, Haoze, Yu, Jiangbo, Koutsopoulos, Haris, Zhao, Jinhua
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

GW-MoE: Resolving Uncertainty in MoE Router with Global Workspace Theory
por: Wu, Haoze, et al.
Publicado: (2024)

GazeFormer-MoE: Context-Aware Gaze Estimation via CLIP and MoE Transformer
por: Zhao, Xinyuan, et al.
Publicado: (2026)

EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference
por: Qian, Yulei, et al.
Publicado: (2024)

STAMImputer: Spatio-Temporal Attention MoE for Traffic Data Imputation
por: Wang, Yiming, et al.
Publicado: (2025)

Grove MoE: Towards Efficient and Superior MoE LLMs with Adjugate Experts
por: Wu, Haoyuan, et al.
Publicado: (2025)

GRACE-MoE: Grouping and Replication with Locality-Aware Routing for Efficient Distributed MoE Inference
por: Han, Yu, et al.
Publicado: (2025)

MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache
por: Xue, Leyang, et al.
Publicado: (2024)

LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
por: Nie, Xiaonan, et al.
Publicado: (2024)

LLaDA-MoE: A Sparse MoE Diffusion Language Model
por: Zhu, Fengqi, et al.
Publicado: (2025)

MoE-Prefill: Zero Redundancy Overheads in MoE Prefill Serving
por: Su, Zhaoyuan, et al.
Publicado: (2026)

MoE-PHDS: One MoE checkpoint for flexible runtime sparsity
por: Hannah, Lauren. A, et al.
Publicado: (2025)

CAT-MoEformer: Context-Aware Temporal MoE Transformer for Beam Prediction
por: Zhou, Changkai, et al.
Publicado: (2026)

MoE-Lightning: High-Throughput MoE Inference on Memory-constrained GPUs
por: Cao, Shiyi, et al.
Publicado: (2024)

KBVQ-MoE: KLT-guided SVD with Bias-Corrected Vector Quantization for MoE Large Language Models
por: Xu, Zukang, et al.
Publicado: (2026)

Accelerating Distributed MoE Training and Inference with Lina
por: Li, Jiamin, et al.
Publicado: (2022)

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
por: Tang, Yehui, et al.
Publicado: (2025)

MoE-Sieve: Routing-Guided LoRA for Efficient MoE Fine-Tuning
por: Manzoni, Andrea
Publicado: (2026)

SP-MoE: Speculative Decoding and Prefetching for Accelerating MoE-based Model Inference
por: Chen, Liangkun, et al.
Publicado: (2025)

OD-MoE: On-Demand Expert Loading for Cacheless Edge-Distributed MoE Inference
por: Wang, Liujianfu, et al.
Publicado: (2025)

LocMoE: A Low-Overhead MoE for Large Language Model Training
por: Li, Jing, et al.
Publicado: (2024)

Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routers
por: Ma, Wenhan, et al.
Publicado: (2025)

MoE-Loco: Mixture of Experts for Multitask Locomotion
por: Huang, Runhan, et al.
Publicado: (2025)

Dense2MoE: Restructuring Diffusion Transformer to MoE for Efficient Text-to-Image Generation
por: Zheng, Youwei, et al.
Publicado: (2025)

BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
por: Ma, Yingjie, et al.
Publicado: (2024)

MoE-GPS: Guidlines for Prediction Strategy for Dynamic Expert Duplication in MoE Load Balancing
por: Ma, Haiyue, et al.
Publicado: (2025)

MoE-Compression: How the Compression Error of Experts Affects the Inference Accuracy of MoE Model?
por: Ma, Songkai, et al.
Publicado: (2025)

MiM-DiT: MoE in MoE with Diffusion Transformers for All-in-One Image Restoration
por: Kong, Lingshun, et al.
Publicado: (2026)

Advancing Expert Specialization for Better MoE
por: Guo, Hongcan, et al.
Publicado: (2025)

MD-Face: MoE-Enhanced Label-Free Disentangled Representation for Interactive Facial Attribute Editing
por: Cui, Xuan, et al.
Publicado: (2026)

GRIN: GRadient-INformed MoE
por: Liu, Liyuan, et al.
Publicado: (2024)

Efficient MoE Serving in the Memory-Bound Regime: Balance Activated Experts, Not Tokens
por: Yu, Yanpeng, et al.
Publicado: (2025)

MoE-Gen: High-Throughput MoE Inference on a Single GPU with Module-Based Batching
por: Xu, Tairan, et al.
Publicado: (2025)

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
por: Liu, Qingxiu, et al.
Publicado: (2026)

HierMoE: Accelerating MoE Training with Hierarchical Token Deduplication and Expert Swap
por: Lin, Wenxiang, et al.
Publicado: (2025)

DynaMo: Runtime Switchable Quantization for MoE with Cross-Dataset Adaptation
por: Zheng, Zihao, et al.
Publicado: (2025)

MoE-SpAc: Efficient MoE Inference Based on Speculative Activation Utility in Heterogeneous Edge Scenarios
por: Li, Shuhuai, et al.
Publicado: (2026)

DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training
por: Jin, Can, et al.
Publicado: (2025)

D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving
por: Wang, Haodong, et al.
Publicado: (2025)

MoE-Lens: Towards the Hardware Limit of High-Throughput MoE LLM Serving Under Resource Constraints
por: Yuan, Yichao, et al.
Publicado: (2025)

MoE-Prism: Disentangling Monolithic Experts for Elastic MoE Services via Model-System Co-Designs
por: Xia, Xinfeng, et al.
Publicado: (2025)