Google maps recommender training divergence as a gradient-weight-loss explosion cascade
_reachsumit · x · 2026-10-14
A Google team's arXiv paper systematically analyzes training instability in large-scale recommender systems, using a short-form video service with billions of daily interactions as the industrial setting.
Key points:
- Divergence follows a "G-W-L cascade": gradient explosion propagates to weight explosion, culminating in loss explosion
- Rapid user-data distribution shifts and rising model complexity are the main triggers
- The paper proposes mapping stabilization interventions to each cascade stage: gradient (G) control, weight/activation (W) stabilization, and loss (L) shaping
- The framework is empirically validated on a major industrial SFV recommender, offering a structured playbook for robust continuous-training deployments in non-stationary environments.
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