Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding

Taowen Liu; Marta Andronic; Deniz Gündüz; George A. Constantinides

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arXiv · 2511.00874

Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding

Taowen Liu·Marta Andronic·Deniz Gündüz·George A. Constantinides

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Abstract

LLM training is resource-intensive. Quantized training improves computational and memory efficiency but introduces quantization noise, which can hinder convergence and degrade model accuracy. Stochastic Rounding (SR) has emerged as a theoretically attractive alternative to deterministic rounding, offering unbiased gradient estimates. However, its interaction with other training factors -- especially batch size -- remains under explored. In this paper, we present a theoretical and empirical study of mini-batch stochastic gradient descent (SGD) with SR, showing that increased batch sizes can compensate for reduced precision during back-propagation. Furthermore, we show that quantizing weights and activations impacts gradient variance in distinct ways. Our experiments validate these theoretical insights.

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Taowen Liu, Marta Andronic, Deniz Gündüz, George A. Constantinides. 2025-11-02. Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding. https://arxiv.org/abs/2511.00874

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