REVIEW 5 cited by
Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Communication-Efficient Language Model Training Scales Reliably and Robustly: Scaling Laws for DiLoCo
read the original abstract
As we scale to more massive machine learning models, the frequent synchronization demands inherent in data-parallel approaches create significant slowdowns, posing a critical challenge to further scaling. Recent work develops an approach (DiLoCo) that relaxes synchronization demands without compromising model quality. However, these works do not carefully analyze how DiLoCo's behavior changes with model size. In this work, we study the scaling law behavior of DiLoCo when training LLMs under a fixed compute budget. We focus on how algorithmic factors, including number of model replicas, hyperparameters, and token budget affect training in ways that can be accurately predicted via scaling laws. We find that DiLoCo scales both predictably and robustly with model size. When well-tuned, DiLoCo scales better than data-parallel training with model size, and can outperform data-parallel training even at small model sizes. Our results showcase a more general set of benefits of DiLoCo than previously documented, including increased optimal batch sizes, improved downstream generalization with scale, and improved evaluation loss for a fixed token budget.
Forward citations
Cited by 5 Pith papers
-
What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity
Local SGD provably improves over Mini-batch SGD under bounded second-order heterogeneity in the general convex setting, with nearly tight upper and lower bounds.
-
LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
LoRDO combines global low-rank projections with full-rank quasi-hyperbolic momentum to let infrequent-synchronization distributed training match low-rank DDP at roughly 10x less communication.
-
Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries
Echelon enables auditable aggregate-only adaptation of language models across privacy boundaries by training locally and sharing only boundary-level aggregates, achieving competitive performance in 1B LoRA experiments.
-
Outer-Momentum Restarting in High-Dimensional Two-Phase Optimization
Periodic outer-momentum restarts in two-phase optimizers exploit phase cancellation in a linearized NTK model to widen stable learning-rate and momentum ranges in language-model pretraining.
-
Does Distributed Training Undermine Compute Governance?
Distributed training may enable evasion of cluster-based compute governance for frontier AI, requiring new detection approaches such as chip tracking and cluster thresholds.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.