Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective
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 mapp...
| Authors | Yuening Li, Zeyu Wang, Zhen Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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