Copertina

ReaL: Efficient RLHF Training of Large Language Models with Parameter Reallocation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Mei, Zhiyu, Fu, Wei, Li, Kaiwei, Wang, Guangju, Zhang, Huanchen, Wu, Yi
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Distributed, Parallel, and Cluster Computing Artificial Intelligence Computation and Language Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand Cores
di: Mei, Zhiyu, et al.
Pubblicazione: (2023)

Optimizing RLHF Training for Large Language Models with Stage Fusion
di: Zhong, Yinmin, et al.
Pubblicazione: (2024)

A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine
di: Li, Anran, et al.
Pubblicazione: (2026)

DeInfer: Efficient Parallel Inferencing for Decomposed Large Language Models
di: Huang, You-Liang, et al.
Pubblicazione: (2026)

ReaLB: Real-Time Load Balancing for Multimodal MoE Inference
di: Wang, Yingping, et al.
Pubblicazione: (2026)

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
di: Yuan, Zhengqing, et al.
Pubblicazione: (2026)

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
di: Che, Tianshi, et al.
Pubblicazione: (2023)

Power Aware Dynamic Reallocation For Inference
di: Jiang, Yiwei, et al.
Pubblicazione: (2026)

AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
di: Yan, Ran, et al.
Pubblicazione: (2025)

LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models
di: Park, Gunho, et al.
Pubblicazione: (2022)

SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
di: Lin, Zheng, et al.
Pubblicazione: (2024)

Towards Federated RLHF with Aggregated Client Preference for LLMs
di: Wu, Feijie, et al.
Pubblicazione: (2024)

Inference Acceleration for Large Language Models on CPUs
di: PS, Ditto, et al.
Pubblicazione: (2024)

ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
di: Yang, Yi, et al.
Pubblicazione: (2025)

Holmes: Towards Distributed Training Across Clusters with Heterogeneous NIC Environment
di: Yang, Fei, et al.
Pubblicazione: (2023)

Efficient Training of Large Language Models on Distributed Infrastructures: A Survey
di: Duan, Jiangfei, et al.
Pubblicazione: (2024)

Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference
di: Shyam, Vasu, et al.
Pubblicazione: (2026)

CO2: Efficient Distributed Training with Full Communication-Computation Overlap
di: Sun, Weigao, et al.
Pubblicazione: (2024)

DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
di: Tian, Ye, et al.
Pubblicazione: (2024)

AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs
di: Lin, Wenxiang, et al.
Pubblicazione: (2026)

Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks
di: Li, Zuguang, et al.
Pubblicazione: (2024)

ZipCCL: Efficient Lossless Data Compression of Communication Collectives for Accelerating LLM Training
di: Lin, Wenxiang, et al.
Pubblicazione: (2026)

HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
di: Yan, Ran, et al.
Pubblicazione: (2024)

Unlocking Full Efficiency of Token Filtering in Large Language Model Training
di: Chai, Di, et al.
Pubblicazione: (2025)

Cascadia: An Efficient Cascade Serving System for Large Language Models
di: Jiang, Youhe, et al.
Pubblicazione: (2025)

CAFL-L: Constraint-Aware Federated Learning with Lagrangian Dual Optimization for On-Device Language Models
di: Zheng, Dongqi, et al.
Pubblicazione: (2025)

RLHFless: Serverless Computing for Efficient RLHF
di: Wei, Rui, et al.
Pubblicazione: (2026)

CFP: Efficient Optimization of Intra-Operator Parallelism Plans for Large Model Training
di: Hu, Weifang, et al.
Pubblicazione: (2025)

DiT-HC: Enabling Efficient Training of Visual Generation Model DiT on HPC-oriented CPU Cluster
di: Zhang, Jinxiao, et al.
Pubblicazione: (2026)

Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow
di: Mei, Yixuan, et al.
Pubblicazione: (2024)

InferCept: Efficient Intercept Support for Augmented Large Language Model Inference
di: Abhyankar, Reyna, et al.
Pubblicazione: (2024)

DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model
di: Gao, Chao, et al.
Pubblicazione: (2024)

DistTrain: Addressing Model and Data Heterogeneity with Disaggregated Training for Multimodal Large Language Models
di: Zhang, Zili, et al.
Pubblicazione: (2024)

InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding
di: Chen, Qiaoling, et al.
Pubblicazione: (2024)

A Scalable Recipe on SuperMUC-NG Phase 2: Efficient Large-Scale Training of Language Models
di: Rajgopal, Ajay Navilarekal, et al.
Pubblicazione: (2026)

Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training
di: Sun, Ao, et al.
Pubblicazione: (2024)

Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo
di: Charles, Zachary, et al.
Pubblicazione: (2025)

FFTrainer: Fast Failover in Large-Language Model Training with Almost-Free State Management
di: Zhao, Bohan, et al.
Pubblicazione: (2025)

Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
di: Wu, Yebo, et al.
Pubblicazione: (2025)

MoLink: Distributed and Efficient Serving Framework for Large Models
di: Jin, Lewei, et al.
Pubblicazione: (2025)