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

69 papers, page 2 of 2

51. NeurIPS 2025Sketched Adaptive Distributed Deep Learning: A Sharp Convergence Analysis
52. NeurIPS 2025Skrull: Towards Efficient Long Context Fine-tuning through Dynamic Data Scheduling
53. NeurIPS 2025SmartCache: Context-aware Semantic Cache for Efficient Multi-turn LLM Inference
54. NeurIPS 2025SpaceServe: Spatial Multiplexing of Complementary Encoders and Decoders for Multimodal LLMs
55. NeurIPS 2025SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMs
56. NeurIPS 2025SpecReason: Fast and Accurate Inference-Time Compute via Speculative Reasoning
57. NeurIPS 2025StarTrail: Concentric Ring Sequence Parallelism for Efficient Near-Infinite-Context Transformer Model Training
58. NeurIPS 2025Subspace Networks: Scaling Decentralized Training with Communication-Efficient Model Parallelism
59. NeurIPS 2025Synergistic Tensor and Pipeline Parallelism
60. NeurIPS 2025Tensor-Parallelism with Partially Synchronized Activations
61. NeurIPS 2025Tight analyses of first-order methods with error feedback
62. NeurIPS 2025Tight Bounds for Maximum Weight Matroid Independent Set and Matching in the Zero Communication Model
63. NeurIPS 2025Tight Bounds on the Distortion of Randomized and Deterministic Distributed Voting
64. NeurIPS 2025Towards Robust Parameter-Efficient Fine-Tuning for Federated Learning
65. NeurIPS 2025Transcending Cost-Quality Tradeoff in Agent Serving via Session-Awareness
66. NeurIPS 2025Understanding outer learning rates in Local SGD
67. NeurIPS 2025Unextractable Protocol Models: Collaborative Training and Inference without Weight Materialization
68. NeurIPS 2025VLM in a flash: I/O-Efficient Sparsification of Vision-Language Model via Neuron Chunking
69. NeurIPS 2025ZeCO: Zero-Communication Overhead Sequence Parallelism for Linear Attention

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.