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arXiv · 2509.22418

Partial Parameter Updates for Efficient Distributed Training

Anastasiia Filippova·Angelos Katharopoulos·David Grangier·Ronan Collobert
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Abstract

We introduce a memory- and compute-efficient method for low-communication distributed training. Existing methods reduce communication by performing multiple local updates between infrequent global synchronizations. We demonstrate that their efficiency can be significantly improved by restricting backpropagation: instead of updating all the parameters, each node updates only a fixed subset while keeping the remainder frozen during local steps. This constraint substantially reduces peak memory usage and training FLOPs, while a full forward pass over all parameters eliminates the need for cross-node activation exchange. Experiments on a $1.3$B-parameter language model trained across $32$ nodes show that our method matches the perplexity of prior low-communication approaches under identical token and bandwidth budgets while reducing training FLOPs and peak memory.

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Anastasiia Filippova, Angelos Katharopoulos, David Grangier, Ronan Collobert. 2025-09-26. Partial Parameter Updates for Efficient Distributed Training. https://arxiv.org/abs/2509.22418

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