{"total":75,"rows":[{"id":"2106.04560","title":"Scaling Vision Transformers","created":"2021-06-08","cited_by_count":999,"area":null},{"id":"2312.03863","title":"Efficient Large Language Models: A Survey","created":"2023-12-06","cited_by_count":186,"area":null},{"id":"2301.11913","title":"SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient","created":"2023-01-27","cited_by_count":59,"area":null},{"id":"2205.02719","title":"Communication-Efficient Adaptive Federated Learning","created":"2022-05-05","cited_by_count":55,"area":null},{"id":"2306.10209","title":"ZeRO++: Extremely Efficient Collective Communication for Giant Model Training","created":"2023-06-16","cited_by_count":52,"area":null},{"id":"2210.06640","title":"Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities","created":"2022-10-13","cited_by_count":38,"area":null},{"id":"2108.06084","title":"The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models","created":"2021-08-13","cited_by_count":36,"area":null},{"id":"2206.01299","title":"Fine-tuning Language Models over Slow Networks using Activation Compression with Guarantees","created":"2022-06-02","cited_by_count":30,"area":null},{"id":"2303.10464","title":"SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models","created":"2023-03-18","cited_by_count":28,"area":null},{"id":"2302.01172","title":"STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition","created":"2023-02-02","cited_by_count":26,"area":null},{"id":"2304.03589","title":"On Efficient Training of Large-Scale Deep Learning Models: A Literature Review","created":"2023-04-07","cited_by_count":26,"area":null},{"id":"2104.06069","title":"1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed","created":"2021-04-13","cited_by_count":24,"area":null},{"id":"2205.05632","title":"On Distributed Adaptive Optimization with Gradient Compression","created":"2022-05-11","cited_by_count":19,"area":null},{"id":"2402.04744","title":"Progressive Gradient Flow for Robust N:M Sparsity Training in Transformers","created":"2024-02-07","cited_by_count":18,"area":null},{"id":"2406.17660","title":"Grass: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients","created":"2024-06-25","cited_by_count":18,"area":null},{"id":"2302.02589","title":"$z$-SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning","created":"2023-02-06","cited_by_count":17,"area":null},{"id":"2401.16265","title":"CO2: Efficient Distributed Training with Full Communication-Computation Overlap","created":"2024-01-29","cited_by_count":17,"area":null},{"id":"2202.06009","title":"Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam","created":"2022-02-12","cited_by_count":15,"area":null},{"id":"2301.09830","title":"Optimus-CC: Efficient Large NLP Model Training with 3D Parallelism Aware Communication Compression","created":"2023-01-24","cited_by_count":15,"area":null},{"id":"2403.06664","title":"Smart-Infinity: Fast Large Language Model Training using Near-Storage Processing on a Real System","created":"2024-03-11","cited_by_count":15,"area":null},{"id":"2409.02231","title":"SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration","created":"2024-09-03","cited_by_count":14,"area":"alignment"},{"id":"2410.15526","title":"SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training","created":"2024-10-20","cited_by_count":14,"area":null},{"id":"2107.01499","title":"BAGUA: Scaling up Distributed Learning with System Relaxations","created":"2021-07-03","cited_by_count":13,"area":null},{"id":"2111.00705","title":"Communication-Compressed Adaptive Gradient Method for Distributed Nonconvex Optimization","created":"2021-11-01","cited_by_count":12,"area":null},{"id":"2506.01260","title":"Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism","created":"2025-06-02","cited_by_count":12,"area":null},{"id":"2505.01043","title":"Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities","created":"2025-05-02","cited_by_count":11,"area":null},{"id":"2507.08784","title":"Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees","created":"2025-07-11","cited_by_count":11,"area":null},{"id":"2111.00856","title":"Large-Scale Deep Learning Optimizations: A Comprehensive Survey","created":"2021-11-01","cited_by_count":10,"area":null},{"id":"2404.06114","title":"Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey","created":"2024-04-09","cited_by_count":10,"area":null},{"id":"2210.07376","title":"ScionFL: Efficient and Robust Secure Quantized Aggregation","created":"2022-10-13","cited_by_count":8,"area":null},{"id":"2306.08881","title":"Evaluation and Optimization of Gradient Compression for Distributed Deep Learning","created":"2023-06-15","cited_by_count":7,"area":null},{"id":"2311.11822","title":"Zero redundancy distributed learning with differential privacy","created":"2023-11-20","cited_by_count":7,"area":null},{"id":"2106.02679","title":"Layered gradient accumulation and modular pipeline parallelism: fast and efficient training of large language models","created":"2021-06-04","cited_by_count":6,"area":null},{"id":"2407.04480","title":"LoCo: Low-Bit Communication Adaptor for Large-scale Model Training","created":"2024-07-05","cited_by_count":6,"area":null},{"id":"2410.23261","title":"$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources","created":"2024-10-30","cited_by_count":6,"area":null},{"id":"2204.06787","title":"Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression","created":"2022-04-14","cited_by_count":5,"area":null},{"id":"2207.03481","title":"Training Transformers Together","created":"2022-07-07","cited_by_count":5,"area":null},{"id":"2310.14423","title":"A Quadratic Synchronization Rule for Distributed Deep Learning","created":"2023-10-22","cited_by_count":5,"area":null},{"id":"2407.19234","title":"Ordered Momentum for Asynchronous SGD","created":"2024-07-27","cited_by_count":5,"area":null},{"id":"2601.22101","title":"ECO: Quantized Training without Full-Precision Master Weights","created":"2026-01-29","cited_by_count":5,"area":"moe"},{"id":"2602.23349","title":"FlashOptim: Optimizers for Memory-Efficient Training","created":"2026-02-26","cited_by_count":5,"area":null},{"id":"2604.15416","title":"StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models","created":"2026-04-16","cited_by_count":5,"area":null},{"id":"2202.10435","title":"Survey on Large Scale Neural Network Training","created":"2022-02-21","cited_by_count":4,"area":null},{"id":"2306.10598","title":"DropCompute: simple and more robust distributed synchronous training via compute variance reduction","created":"2023-06-18","cited_by_count":4,"area":null},{"id":"2406.13936","title":"Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods","created":"2024-06-20","cited_by_count":4,"area":null},{"id":"2411.12898","title":"Problem-dependent convergence bounds for randomized linear gradient compression","created":"2024-11-19","cited_by_count":3,"area":null},{"id":"2211.00100","title":"Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms","created":"2022-10-31","cited_by_count":2,"area":null},{"id":"2302.06103","title":"FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging","created":"2023-02-13","cited_by_count":2,"area":null},{"id":"2303.01341","title":"Matching-based Term Semantics Pre-training for Spoken Patient Query Understanding","created":"2023-03-02","cited_by_count":2,"area":null},{"id":"2409.13198","title":"Exploring Scaling Laws for Local SGD in Large Language Model Training","created":"2024-09-20","cited_by_count":2,"area":null},{"id":"2412.10663","title":"Memory-Efficient 4-bit Preconditioned Stochastic Optimization","created":"2024-12-14","cited_by_count":2,"area":null},{"id":"2511.10333","title":"EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training","created":"2025-11-13","cited_by_count":2,"area":null},{"id":"2202.12831","title":"Benchmark Assessment for DeepSpeed Optimization Library","created":"2022-02-12","cited_by_count":1,"area":null},{"id":"2210.07454","title":"Communication-Efficient Adam-Type Algorithms for Distributed Data Mining","created":"2022-10-14","cited_by_count":1,"area":null},{"id":"2309.13307","title":"CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity","created":"2023-09-23","cited_by_count":1,"area":null},{"id":"2405.15778","title":"Investigation of Energy-efficient AI Model Architectures and Compression Techniques for \"Green\" Fetal Brain Segmentation","created":"2024-04-03","cited_by_count":1,"area":null},{"id":"2405.17932","title":"Towards Communication-efficient Federated Learning via Sparse and Aligned Adaptive Optimization","created":"2024-05-28","cited_by_count":1,"area":null},{"id":"2411.06770","title":"Sketched Adaptive Federated Deep Learning: A Sharp Convergence Analysis","created":"2024-11-11","cited_by_count":1,"area":null},{"id":"2411.16462","title":"Lion Cub: Minimizing Communication Overhead in Distributed Lion","created":"2024-11-25","cited_by_count":1,"area":null},{"id":"2505.23523","title":"Efficient AllReduce with Stragglers","created":"2025-05-29","cited_by_count":1,"area":null},{"id":"2509.22418","title":"Partial Parameter Updates for Efficient Distributed Training","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2605.11396","title":"MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2606.05484","title":"Learned Subspace Compression for Communication-Efficient Pipeline Parallelism","created":"2026-06-03","cited_by_count":1,"area":null},{"id":"2606.13894","title":"Gefen: Optimized Stochastic Optimizer","created":"2026-06-11","cited_by_count":1,"area":null},{"id":"2108.08677","title":"Order Optimal Bounds for One-Shot Federated Learning over non-Convex Loss Functions","created":"2021-08-19","cited_by_count":0,"area":null},{"id":"2109.12400","title":"Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis","created":"2021-09-25","cited_by_count":0,"area":null},{"id":"2312.02182","title":"Adam-like Algorithm with Smooth Clipping Attains Global Minima: Analysis Based on Ergodicity of Functional SDEs","created":"2023-11-29","cited_by_count":0,"area":null},{"id":"2403.00952","title":"MediSwift: Efficient Sparse Pre-trained Biomedical Language Models","created":"2024-03-01","cited_by_count":0,"area":null},{"id":"2407.00467","title":"VcLLM: Video Codecs are Secretly Tensor Codecs","created":"2024-06-29","cited_by_count":0,"area":null},{"id":"2602.08007","title":"From $O(mn)$ to $O(r^2)$: Two-Sided Low-Rank Communication for Adam in Distributed Training with Memory Efficiency","created":"2026-02-08","cited_by_count":0,"area":null},{"id":"2604.25550","title":"Enhancing SignSGD: Small-Batch Convergence Analysis and a Hybrid Switching Strategy","created":"2026-04-28","cited_by_count":0,"area":null},{"id":"2605.00539","title":"AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2607.07494","title":"GeoFP8: Geometry-Aware FP8 Gradient Compression for Distributed LLM Training","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2609.36662","title":"AutoLoCo: Communication Efficient Distributed LLM Training via Adaptive Synchronization","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.14277","title":"Echo: Error-Compensated 4-Bit Tensor-Parallel Communication for LLM Training and Inference","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}