CGAD is a staleness-aware Adam variant for DiLoCo that gates gradients with cosine and exponential decay, proves a convergence bound independent of maximum delay, and demonstrates stable pretraining of 25M to 7B parameter Llama-style models across controlled delays.
Streaming diloco with overlapping communication: Towards a distributed free lunch
8 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 8representative citing papers
FoMoE partitions expert layers across workers in MoE LLMs, skips non-resident experts, and reports up to 1.42x lower communication than baselines plus 1.4x throughput gains while maintaining stable routing.
GASLoC generalizes communication acceleration to the outer optimizer to enable gossip-based decentralized LLM pretraining that supports adaptive optimizers, local steps, and outperforms prior decentralized methods on standard tasks while matching DiLoCo in multi-step regimes.
ResBM achieves 128x activation compression in pipeline-parallel transformer training by adding a residual bottleneck module that preserves a low-rank identity path, with no major loss in convergence or added overhead.
TAH-Quant introduces tile-wise adaptive Hadamard quantization for activations in pipeline parallelism, achieving 3-4 bit compression with up to 4.3x throughput speedup and O(1/sqrt(T)) convergence matching SGD.
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.
HeLoCo corrects misaligned pseudo-gradients in asynchronous low-communication training via outer momentum reference, yielding up to 7.5% better loss at fixed tokens and 22.1% over synchronous under severe heterogeneity.
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.
citing papers explorer
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Cosine-Gated Adam-Decay: Drop-In Staleness-Aware Outer Optimization for Decoupled DiLoCo
CGAD is a staleness-aware Adam variant for DiLoCo that gates gradients with cosine and exponential decay, proves a convergence bound independent of maximum delay, and demonstrates stable pretraining of 25M to 7B parameter Llama-style models across controlled delays.
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FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs
FoMoE partitions expert layers across workers in MoE LLMs, skips non-resident experts, and reports up to 1.42x lower communication than baselines plus 1.4x throughput gains while maintaining stable routing.
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Unifying Local Communications and Local Updates for LLM Pretraining
GASLoC generalizes communication acceleration to the outer optimizer to enable gossip-based decentralized LLM pretraining that supports adaptive optimizers, local steps, and outperforms prior decentralized methods on standard tasks while matching DiLoCo in multi-step regimes.
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ResBM: Residual Bottleneck Models for Low-Bandwidth Pipeline Parallelism
ResBM achieves 128x activation compression in pipeline-parallel transformer training by adding a residual bottleneck module that preserves a low-rank identity path, with no major loss in convergence or added overhead.
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TAH-QUANT: Effective Activation Quantization in Pipeline Parallelism over Slow Network
TAH-Quant introduces tile-wise adaptive Hadamard quantization for activations in pipeline parallelism, achieving 3-4 bit compression with up to 4.3x throughput speedup and O(1/sqrt(T)) convergence matching SGD.
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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.
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HeLoCo: Efficient asynchronous low-communication training under data and device heterogeneity
HeLoCo corrects misaligned pseudo-gradients in asynchronous low-communication training via outer momentum reference, yielding up to 7.5% better loss at fixed tokens and 22.1% over synchronous under severe heterogeneity.
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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.