Distributed Training papers, 2023–2026

Key findings

• Distributed Training went from 95 papers in 2023 to 98 in 2026, roughly 1.0x in three years.
• NeurIPS publishes the most Distributed Training work: 208 papers from 2023 to 2026, 53% of the four-venue total.
• The fastest-growing overlap is with Multimodal LLMs: papers tagged with both rose from 1 in 2023 to 10 in 2026.
• Among the 5 topics under Efficiency and Systems, it ranks #4 by 2026 paper count; the fastest grower in the group, Hardware-Aware ML, is up 4.4x versus 1.0x here.

Representative papers

Spread across years and venues so every edition shows up

• NeurIPS 2026AgentWeave: Efficient Distributed Agent Serving via Flow Decomposition
• ICML 2026Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
• ICLR 2026Cascadia: An Efficient Cascade Serving System for Large Language Models
• CVPR 2026Finding Distributed Object-Centric Properties in Self-Supervised Transformers
• NeurIPS 2025A Fair Federated Learning Method for Handling Client Participation Probability Inconsistencies in Heterogeneous Environments
• ICML 2025BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach
• ICLR 2025Addressing Label Shift in Distributed Learning via Entropy Regularization
• CVPR 2025Real-time High-fidelity Gaussian Human Avatars with Position-based Interpolation of Spatially Distributed MLPs
• NeurIPS 2024Communication Efficient Distributed Training with Distributed Lion
• ICML 2024Embarrassingly Parallel GFlowNets
• ICLR 2024Benchmarking Algorithms for Federated Domain Generalization
• CVPR 2024Deciphering 'What' and 'Where' Visual Pathways from Spectral Clustering of Layer-Distributed Neural Representations
• NeurIPS 20232Direction: Theoretically Faster Distributed Training with Bidirectional Communication Compression
• ICML 2023Block Subsampled Randomized Hadamard Transform for Nyström Approximation on Distributed Architectures
• ICLR 2023Distributed Extra-gradient with Optimal Complexity and Communication Guarantees
• CVPR 2023DisCo-CLIP: A Distributed Contrastive Loss for Memory Efficient CLIP Training
• NeurIPS 2026Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods
• ICML 2026Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers
• ICLR 2026DualMap: Enabling Both Cache Affinity and Load Balancing for Distributed LLM Serving
• CVPR 2026Parallel Rigidity Matters for Bundle Adjustment

All Distributed Training papers

208 papers, page 2 of 5

51. NeurIPS 2025Distance Adaptive Beam Search for Provably Accurate Graph-Based Nearest Neighbor Search
52. NeurIPS 2025Distributed mediation analysis with communication efficiency
53. NeurIPS 2025Distributed Multi-Agent Bandits Over Erdős-Rényi Random Networks
54. NeurIPS 2025DUO: No Compromise to Accuracy Degradation
55. NeurIPS 2025DynaPipe: Dynamic Layer Redistribution for Efficient Serving of LLMs with Pipeline Parallelism
56. NeurIPS 2025Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
57. NeurIPS 2025Efficient Training-Free Online Routing for High-Volume Multi-LLM Serving
58. NeurIPS 2025ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism
59. NeurIPS 2025EnCompass: Enhancing Agent Programming with Search Over Program Execution Paths
60. NeurIPS 2025Exploiting Dynamic Sparsity in Einsum
61. NeurIPS 2025FedLPA: Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
62. NeurIPS 2025First Attentions Last: Better Exploiting First Attentions for Efficient Parallel Training
63. NeurIPS 2025FlashMoE: Fast Distributed MoE in a Single Kernel
64. NeurIPS 2025FlowMoE: A Scalable Pipeline Scheduling Framework for Distributed Mixture-of-Experts Training
65. NeurIPS 2025Foresight: Adaptive Layer Reuse for Accelerated and High-Quality Text-to-Video Generation
66. NeurIPS 2025Gatekeeper: Improving Model Cascades Through Confidence Tuning
67. NeurIPS 2025Generative Caching for Structurally Similar Prompts and Responses
68. NeurIPS 2025HyGen: Efficient LLM Serving via Elastic Online-Offline Request Co-location
69. NeurIPS 2025InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction
70. NeurIPS 2025KVFlow: Efficient Prefix Caching for Accelerating LLM-Based Multi-Agent Workflows
71. NeurIPS 2025Loquetier: A Virtualized Multi-LoRA Framework for Unified LLM Fine-tuning and Serving
72. NeurIPS 2025MeCeFO: Enhancing LLM Training Robustness via Fault-Tolerant Optimization
73. NeurIPS 2025Mixtures of Subspaces for Bandwidth Efficient Context Parallel Training
74. NeurIPS 2025Mozart: Modularized and Efficient MoE Training on 3.5D Wafer-Scale Chiplet Architectures
75. NeurIPS 2025msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML
76. NeurIPS 2025MUSTAFAR: Promoting Unstructured Sparsity for KV Cache Pruning in LLM Inference
77. NeurIPS 2025NestedFP: High-Performance, Memory-Efficient Dual-Precision Floating Point Support for LLMs
78. NeurIPS 2025PipeFusion: Patch-level Pipeline Parallelism for Diffusion Transformers Inference
79. NeurIPS 2025Progressive Data Dropout: An Embarrassingly Simple Approach to Train Faster
80. NeurIPS 2025PubSub-VFL: Towards Efficient Two-Party Split Learning in Heterogeneous Environments via Publisher/Subscriber Architecture
81. NeurIPS 2025Quartet: Native FP4 Training Can Be Optimal for Large Language Models
82. NeurIPS 2025REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
83. NeurIPS 2025Refinement Methods for Distributed Distribution Estimation under $\ell^p$-Losses
84. NeurIPS 2025Repo2Run: Automated Building Executable Environment for Code Repository at Scale
85. NeurIPS 2025Robust Distributed Estimation: Extending Gossip Algorithms to Ranking and Trimmed Means
86. NeurIPS 2025SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training
87. NeurIPS 2025Sketched Adaptive Distributed Deep Learning: A Sharp Convergence Analysis
88. NeurIPS 2025Skrull: Towards Efficient Long Context Fine-tuning through Dynamic Data Scheduling
89. NeurIPS 2025SmartCache: Context-aware Semantic Cache for Efficient Multi-turn LLM Inference
90. NeurIPS 2025SpaceServe: Spatial Multiplexing of Complementary Encoders and Decoders for Multimodal LLMs
91. NeurIPS 2025SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs
92. NeurIPS 2025SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
93. NeurIPS 2025StarTrail: Concentric Ring Sequence Parallelism for Efficient Near-Infinite-Context Transformer Model Training
94. NeurIPS 2025Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism
95. NeurIPS 2025Synergistic Tensor and Pipeline Parallelism
96. NeurIPS 2025Tensor-Parallelism with Partially Synchronized Activations
97. NeurIPS 2025Tight analyses of first-order methods with error feedback
98. NeurIPS 2025Tight Bounds for Maximum Weight Matroid Independent Set and Matching in the Zero Communication Model
99. NeurIPS 2025Tight Bounds on the Distortion of Randomized and Deterministic Distributed Voting
100. NeurIPS 2025Towards Robust Parameter-Efficient Fine-Tuning for Federated Learning

FAQ

How many Distributed Training papers were published at NeurIPS, ICLR, ICML and CVPR from 2023 to 2026?

396 in total: 95 in 2023, 74 in 2024, 129 in 2025, 98 in 2026.

How many Distributed Training papers are at NeurIPS 2026?

NeurIPS 2026 has 36 Distributed Training papers, versus 69 the year before.

How many Distributed Training papers are at ICLR 2026?

ICLR 2026 has 18 Distributed Training papers, versus 18 the year before.

How many Distributed Training papers are at ICML 2026?

ICML 2026 has 42 Distributed Training papers, versus 40 the year before.

How many Distributed Training papers are at CVPR 2026?

CVPR 2026 has 2 Distributed Training papers, versus 2 the year before.

Which conference publishes the most Distributed Training papers?

NeurIPS, with 208 papers from 2023 to 2026.