The increasing complexity of modern deep neural network models and the
expanding sizes of datasets necessitate the development of optimized and
scalable training methods. In this white paper, we addressed the challenge of
efficiently training neural network models using sequences of varying sizes. To
address this challenge, we propose a novel training scheme that enables
efficient distributed data-parallel training on sequences of different sizes
with minimal overhead. By using this scheme we were able to reduce the padding
amount by more than 100$x$ while not deleting a single frame, resulting in an
overall increased performance on both training time and Recall in our
experiments.

使用新的训练方案，能够在不同大小的序列上实现高效的分布式数据并行训练，最小化内存消耗，并在实验中取得了整体性能的提升。

BLoad：增强神经网络训练的高效顺序数据处理

BLoad: Enhancing Neural Network Training with Efficient Sequential Data  Handling

Recent work in incremental learning has introduced diverse approaches to
tackle catastrophic forgetting from data augmentation to optimized training
regimes. However, most of them focus on very few training steps. We propose a
method for robust incremental learning over dozens of fine-tuning steps using
data from a variety of languages. We show that a combination of
data-augmentation and an optimized training regime allows us to continue
improving the model even for as many as fifty training steps. Crucially, our
augmentation strategy does not require retaining access to previous training
data and is suitable in scenarios with privacy constraints.

利用来自各种语言的数据，我们提出了一种方法，通过数据增强和优化训练，实现在数十个微调步骤上的增量式学习，我们的增强策略无需保留到以前的训练数据，适用于有隐私约束的情况。