Bigger Is Not Always Better: Towards Effective Scaling of Sequential Recommendation

Scaling model size has become an increasingly important approach to improving sequential recommendation. However, prior studies suggest that simply increasing model size does not necessarily lead to consistent performance improvements. As recommenders scale, additional model capacity does not necessarily yield proportional performance gains, while optimization becomes progressively more challenging. Therefore, a central challenge is to improve scaling efficiency and sustain consistent performance gains as model size increases. To address these, we propose TriScaleRec, a scaling-oriented framework for sequential recommendation. Specifically, TriScaleRec improves depth utilization through adaptive cross-depth representation aggregation. On this basis, it expands model capacity with sparse latent-space expert computation while controlling active computation, and further employs structure-aware optimization to sustain effective training at larger scales. Experiments on multiple public benchmarks show that TriScaleRec achieves strong recommendation performance while scaling experiments over six progressively larger model configurations further show that its performance continues to improve more consistently as model size increases.