Recommender systems play an important role in many content platforms. While most recommendation research is dedicated to designing better models to improve user experience, we found that research on stabilizing the training for such models is severely under-explored. As recommendation models become larger and more sophisticated, they are more susceptible to training instability issues, i.e., loss divergence, which can make the model unusable, waste significant resources and block model developments. In this paper, we share our findings and best practices we learned for improving the training stability of a real-world multitask ranking model for YouTube recommendations. We show some properties of the model that lead to unstable training and conjecture on the causes. Furthermore, based on our observations of training dynamics near the point of training instability, we hypothesize why existing solutions would fail, and propose a new algorithm to mitigate the limitations of existing solutions. Our experiments on YouTube production dataset show the proposed algorithm can significantly improve training stability while not compromising convergence, comparing with several commonly used baseline methods.

翻译：推荐系统在许多内容平台中发挥着重要作用。尽管大多数推荐研究致力于设计更好的模型以提升用户体验，但我们发现针对此类模型训练稳定性的研究严重不足。随着推荐模型变得更大、更复杂，它们更容易出现训练不稳定性问题（即损失发散），这可能导致模型不可用、浪费大量资源并阻碍模型开发。本文分享了我们在改进YouTube推荐实际多任务排序模型训练稳定性方面的发现与最佳实践。我们揭示了导致训练不稳定的若干模型特性，并推测其成因。此外，基于训练不稳定点附近的训练动态观察，我们假设现有解决方案失效的原因，并提出一种新算法以缓解现有方案的局限性。在YouTube生产数据集上的实验表明，与几种常用基线方法相比，所提算法能在不牺牲收敛性的前提下显著提升训练稳定性。