Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective

Abstract:Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions, faces critical stability challenges. Rapid distribution shifts in user data and increasing model complexity frequently trigger a divergence cascade, typically initiating with gradient explosion, propagating to weight explosion, and culminating in loss explosion. This G-W-L cascade severely impedes model convergence and performance. This paper provides a systematic analysis of this G-W-L cascade within an industrial SFV recommender context. We then propose and evaluate a structured mitigation framework where interventions are explicitly mapped to counteract specific stages of the cascade: gradient (G) control, weight/activation (W) stabilization, and loss (L) shaping. Our contribution provides a structured framework for understanding and tackling training divergence, offering practical insights for deploying robust, continuously updated recommenders in large-scale, non-stationary settings. We empirically validate the effectiveness of these strategies on a major industrial SFV recommendation system.

Information Retrieval (cs.IR); Machine Learning (cs.LG)

arXiv:2610.14394 [cs.IR]

(or arXiv:2610.14394v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2610.14394 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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