SPARKLING：在宽度递进学习中实现信号保持与对称性破缺的平衡

摘要

渐进式学习（PL）通过逐步扩展模型规模来降低预训练计算开销。虽然前人研究已深入探索了深度扩展，但宽度扩展的研究仍明显不足，现有少数方法也仅限于训练早期阶段。然而，在训练中期进行宽度扩展对于最大化计算效率至关重要，但由于严重的训练不稳定性，这仍是艰巨挑战。实验表明，在此阶段采用简单初始化会破坏激活统计量，引发损失值突变；而基于复制的初始化则会导致梯度对称性，阻碍特征多样性。为解决这些问题，我们提出SPARKLING框架（通过平衡信号保持与对称性破缺实现宽度渐进学习），这是一种适用于中期宽度扩展的创新方案。该方法通过RMS尺度一致性实现信号保持，在扩展过程中稳定激活统计量；通过非对称优化器状态重置与学习率重新预热确保对称性破缺。在混合专家模型上的大量实验表明，SPARKLING在多种宽度维度和优化器族中均显著优于从头训练的方法，在2倍宽度扩展下最高可降低35%的训练成本。

English

Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansion remains significantly understudied, with the few existing methods limited to the early stages of training. However, expanding width during the mid-stage is essential for maximizing computational savings, yet it remains a formidable challenge due to severe training instabilities. Empirically, we show that naive initialization at this stage disrupts activation statistics, triggering loss spikes, while copy-based initialization introduces gradient symmetry that hinders feature diversity. To address these issues, we propose SPARKLING (balancing {S}ignal {P}reservation {A}nd symmet{R}y brea{K}ing for width-progressive {L}earn{ING}), a novel framework for mid-stage width expansion. Our method achieves signal preservation via RMS-scale consistency, stabilizing activation statistics during expansion. Symmetry breaking is ensured through asymmetric optimizer state resetting and learning rate re-warmup. Extensive experiments on Mixture-of-Experts (MoE) models demonstrate that, across multiple width axes and optimizer families, SPARKLING consistently outperforms training from scratch and reduces training cost by up to 35% under 2times width expansion.

SPARKLING：在宽度递进学习中实现信号保持与对称性破缺的平衡

SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning

摘要

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