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DES-LOC: Desynced Low Communication Adaptive Optimizers for Foundation Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Distributed TrainingFoundation ModelsLarge Language ModelsOptimizersCommunication EfficiencyFederated LearningDistributed SystemsOptimization TheoryScalingRobustness
Abstract:

Scaling foundation model training with Distributed Data Parallel~(DDP) methods is bandwidth-limited. Existing infrequent communication methods like Local SGD were designed to synchronize model parameters only and cannot be trivially applied to adaptive optimizers due to additional optimizer states. Heuristic approaches that keep states local or reset them lack guarantees and can be unstable in compute‑efficient batch regimes; conversely, Local Adam synchronizes all states uniformly and is provably convergent but triples communication costs. We propose Desynced Low Communication Adaptive Optimizers (DES-LOC), a family of optimizers assigning independent synchronization periods to parameters and momenta, enabling lower communication costs while preserving convergence. Our theoretical analysis shows that while parameter synchronization dominates the asymptotic rate in-expectation, high-probability convergence guarantees require at least infrequent synchronization of the second momentum. Furthermore, we prove that more frequent momentum sync permits larger stable step sizes. Experiments on language models of up to 1.7B show that DES-LOC can communicate 170x less than DDP and 2x less than the previous state-of-the-art Local Adam, enabling 1.3x–2.1x wall‑clock speedups over DDP for 1-13B models on 100Gb/s links. Furthermore, unlike previous heuristic methods, DES-LOC is robust to worker failures offering a scalable, efficient, and fault-tolerant solution for foundation model training.

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Overview

Overall Novelty Assessment

The paper proposes DES-LOC, a family of adaptive optimizers that assign independent synchronization periods to parameters and momenta to reduce communication costs in distributed training. It sits within the Asynchronous and Local Update Methods leaf, which contains only three papers including this one. This is a relatively sparse research direction compared to more crowded areas like Gradient and Activation Compression (four papers) or Wireless Network Integration (five papers), suggesting the specific problem of desynchronized optimizer state updates remains underexplored within the broader distributed training landscape.

The taxonomy reveals that neighboring leaves pursue complementary strategies: Fine-Grained Overlap and Kernel Fusion (three papers) focuses on hiding latency through scheduling, while Cross-Region Training (one paper) addresses wide-area network challenges. The parent branch Communication-Computation Overlap and Scheduling excludes pure compression methods, which are handled separately under Communication Compression. DES-LOC's approach of varying synchronization frequencies for different optimizer components bridges asynchronous methods and optimizer state management, connecting to Weight and Optimizer State Compression but diverging by focusing on scheduling rather than compression.

Among 25 candidates examined, the first contribution (independent synchronization periods) shows two refutable candidates from six examined, indicating some prior work on related optimizer synchronization strategies. The convergence theory contribution examined nine candidates with none clearly refuting it, suggesting theoretical novelty within the limited search scope. The empirical validation examined ten candidates without refutation, though this reflects the specific 170× reduction claim rather than exhaustive comparison with all communication-reduction methods. The analysis covers top-K semantic matches and citation expansion, not the entire field.

Given the sparse taxonomy leaf and limited refutation across contributions, the work appears to occupy a relatively novel position within the examined literature. However, the two refutable candidates for the core optimizer design suggest some conceptual overlap exists. The scope limitations mean adjacent research directions or recent preprints may contain additional relevant prior work not captured in this 25-candidate analysis.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
25
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: distributed training of foundation models with reduced communication overhead. The field addresses the challenge of scaling large-scale model training across multiple devices while minimizing the communication bottleneck that often dominates training time. The taxonomy reveals several complementary strategies: Communication Compression and Gradient Reduction Techniques focus on reducing the volume of data exchanged through methods like gradient quantization and sparsification, as seen in Deep Gradient Compression[25] and Compression Assisted Allgather[23]. Communication-Computation Overlap and Scheduling aims to hide communication latency by overlapping it with computation, exemplified by Co2 Overlap[14] and Cross-region Overlapping[29]. Parallelism Strategy Design explores hybrid parallelism configurations that balance computation and communication trade-offs, with works like Megatron-lm[46] and SWARM Parallelism[3] demonstrating different partitioning approaches. Production-Scale System Implementations such as MegaScale[8] and Megascale MoE[10] integrate multiple techniques for real-world deployments, while Federated Foundation Model Training addresses decentralized scenarios with heterogeneous devices, as in PromptFL[11] and Edge Foundation Finetuning[22]. General frameworks and surveys like Efficient LLM Training Survey[1] provide broader perspectives on the landscape. A particularly active line of work explores asynchronous and local update methods that reduce synchronization frequency, trading off some coordination for substantial communication savings. DES-LOC[0] falls within this branch alongside SlowMo[32] and DiLoCo Scaling Laws[38], which investigate how infrequent global synchronization with local SGD steps affects convergence and scalability. While SlowMo[32] introduced momentum-based slow updates to stabilize training, DiLoCo Scaling Laws[38] examines how these methods scale with model size and cluster topology. DES-LOC[0] emphasizes a different angle by focusing on decentralized strategies that adapt synchronization patterns dynamically, contrasting with the more structured periodic updates in DiLoCo[38]. This cluster of methods represents a shift from traditional synchronous data parallelism toward more flexible, communication-efficient paradigms that are especially valuable when training across geographically distributed or bandwidth-constrained environments.

Claimed Contributions

DES-LOC optimizer family with independent synchronization periods

The authors introduce DES-LOC, a new family of adaptive optimizers that assigns different synchronization frequencies to model parameters and optimizer momentum states. This design reduces communication overhead compared to existing methods while maintaining theoretical convergence guarantees.

6 retrieved papers
Can Refute
Convergence theory for desynchronized parameter and momentum updates

The authors provide theoretical convergence guarantees for DES-LOC under non-convex objectives for SGDM and weakly convex objectives for Adam. Their analysis shows that parameter synchronization dominates the asymptotic convergence rate, while momentum synchronization frequency affects stable step sizes and high-probability bounds.

9 retrieved papers
Empirical validation showing 170× communication reduction over DDP

The authors demonstrate through experiments on language models up to 1.7B parameters that DES-LOC achieves substantial communication reductions: 170× compared to standard DDP and 2× compared to Local Adam, resulting in significant wall-clock speedups while maintaining competitive performance on in-context learning benchmarks.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DES-LOC optimizer family with independent synchronization periods

The authors introduce DES-LOC, a new family of adaptive optimizers that assigns different synchronization frequencies to model parameters and optimizer momentum states. This design reduces communication overhead compared to existing methods while maintaining theoretical convergence guarantees.

Contribution

Convergence theory for desynchronized parameter and momentum updates

The authors provide theoretical convergence guarantees for DES-LOC under non-convex objectives for SGDM and weakly convex objectives for Adam. Their analysis shows that parameter synchronization dominates the asymptotic convergence rate, while momentum synchronization frequency affects stable step sizes and high-probability bounds.

Contribution

Empirical validation showing 170× communication reduction over DDP

The authors demonstrate through experiments on language models up to 1.7B parameters that DES-LOC achieves substantial communication reductions: 170× compared to standard DDP and 2× compared to Local Adam, resulting in significant wall-clock speedups while maintaining competitive performance on in-context learning benchmarks.

Table of Contents