arXiv Machine Learning
Jul 24
Overcoming the Communication-Performance Tradeoff in LLM Pretraining
arXiv:2508. 15706v3 Announce Type: replace Abstract: Communication-efficient distributed training algorithms (e.
Can Model Merging Improve Aggregation in DiLoCo?
arXiv:2607. 03011v1 Announce Type: cross Abstract: Model merging techniques, which aggregate independently finetuned models into one to combine their capabilities, have become a topic of significant interest in recent years, with a broad array of methods having been proposed to tackle this problem.
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arXiv Machine Learning
Jun 9
TAMUNA: Doubly Accelerated Distributed Optimization under Partial Participation
arXiv:2302. 09832v4 Announce Type: replace Abstract: In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck.
arXiv Machine Learning
Jun 2
Decentralized Instruction Tuning: Conflict-Aware Splitting and Weight Merging
arXiv:2606. 01717v1 Announce Type: new Abstract: Instruction tuning aligns large language models, including multimodal ones, with diverse user intents, but scaling to heterogeneous mixtures is hindered by gradient interference and bandwidth-heavy synchronization.
Hugging Face Trending Papers
Jun 9
Unifying Local Communications and Local Updates for LLM Pretraining
Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data centers, and lower-bandwidth links. Many practical methods reduce communication frequency but still rely on synchronous All-Reduce operations that maintain identical model states and tie progress to global collectives.
arXiv AI
Jun 10
Unifying Local Communications and Local Updates for LLM Pretraining
arXiv:2606. 11081v1 Announce Type: cross Abstract: Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data centers, and lower-bandwidth links.
arXiv Machine Learning
Jun 2
Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning
arXiv:2606. 01128v1 Announce Type: new Abstract: Communication overhead is a crucial bottleneck in scalable distributed learning.
arXiv Machine Learning
Jun 3
MuLoCo: Muon is a practical inner optimizer for DiLoCo
arXiv:2505. 23725v3 Announce Type: replace Abstract: DiLoCo is a powerful framework for training large language models (LLMs), enabling larger optimal batch sizes and increased accelerator utilization under networking constraints.
arXiv Machine Learning
Jul 3
SCAPE: Accurate and Efficient LLM Training with Extreme Sparse Communication
arXiv:2607. 01678v1 Announce Type: new Abstract: Communication increasingly dominates the cost of Large Language Model (LLM) pre-training, especially under data-parallel and sharded training schemes, where gradient synchronization and parameter reconstruction overhead increase with model size and system scale.
arXiv Machine Learning
Jul 15
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication
arXiv:2405. 11667v2 Announce Type: replace Abstract: Local SGD is a popular optimization method in distributed learning, often outperforming other algorithms in practice, including mini-batch SGD.
arXiv AI
Jun 19
LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
arXiv:2602. 04396v2 Announce Type: replace-cross Abstract: Distributed training of foundation models via $\texttt{DDP}$ is limited by interconnect bandwidth.
Hugging Face Trending Papers
Jul 15
Dysco: Dynamic Subspace Boosting to Mitigate LoRA Interference in Federated Learning
Federated fine-tuning of large pre-trained models increasingly relies on Low-Rank Adaptation (LoRA) to reduce communication and computation, but heterogeneous clients can make adapter aggregation unstable. We identify the data-parameter interference as a geometric source of this instability.
arXiv Machine Learning
Jun 18
PACT: Preserving Anchored Cores in Task-vectors for Model Merging
arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.