Mitigating Early Training Collapse in CTR Models
Deep neural models for click-through rate prediction often exhibit a sharp decline in validation performance immediately after the first training epoch despite continued improvement in training loss. This instability restricts effective learning and limits model performance. In this study, we analyze this behavior using large-scale industrial datasets and evaluate practical mitigation strategies. While reducing the learning rate provides only incremental gains, controlling feature sparsity yields substantial improvements. Removing highly sparse features and aggregating infrequent feature values stabilizes training, extends useful learning beyond a single epoch, and improves both offline e...
| Authors | Ergun Biçici, Erkan Çetinyamaç |
|---|---|
| Primary category | cs.LG |
| Published | 20 June 2026 |
| Last updated at source | 20 June 2026 |
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