用最优传输对齐神经元，高效扩展大模型深度
Progressive Depth Up-scaling via Optimal Transport
- 通过最优传输对齐相邻层神经元，生成新层
- 扩展后在不同模型上训练效率更高，性能更好
- 越靠近顶层插入新层，训练越快且增益更大
扩大语言模型规模可提升性能，但训练成本高。深度扩展通过向预训练模型添加新层实现更高效的训练。然而，现有方法多采用复制或平均基础层权重，忽略神经元排列差异，可能引发错位从而损害性能。受最优传输（OT）用于神经元对齐的启发，我们提出最优传输深度扩展（OpT-DeUS）。该方法利用最优传输对齐并融合相邻基础层的Transformer模块，以创建新层，缓解层间神经元排列不一致问题。在持续预训练和监督微调中，OpT-DeUS在不同模型尺寸下均实现更优的整体性能与更高的训练效率。进一步分析表明，将新层插入位置越接近顶层，反向传播时间越短，训练效率越高，同时获得额外性能提升。
原文摘要 · Abstract (English)
Scaling Large Language Models (LLMs) yields performance gains but incurs substantial training costs. Depth up-scaling offers training efficiency by adding new layers to pre-trained models. However, most existing methods copy or average weights from base layers, neglecting neuron permutation differences. This limitation can potentially cause misalignment that harms performance. Inspired by applying Optimal Transport (OT) for neuron alignment, we propose Optimal Transport Depth Up-Scaling (OpT-DeUS). OpT-DeUS aligns and fuses Transformer blocks in adjacent base layers via OT for new layer creation, to mitigate neuron permutation mismatch between layers. OpT-DeUS achieves better overall performance and offers improved training efficiency than existing methods for continual pre-training and supervised fine-tuning across different model sizes. To further evaluate the impact of interpolation positions, our extensive analysis shows that inserting new layers closer to the top results in higher training efficiency due to shorter back-propagation time while obtaining additional performance gains.
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