What is the correlation between repeated-training costs and score stability when scaling generative recommenda

Abstract

Abstract: Generative recommendation models can model user behavior as sequences of events and provide a shared backbone for multiple recommendation tasks. In production, however, pre-training gains do not automatically translate into downstream application improvements: task headroom, repeated-training cost, serving latency, and item freshness all affect transfer. We describe our experience scaling a generative recommender from 2M to 1B backbone parameters, excluding embedding and decoding layers, in a production-scale title recommendation setting. Across multiple downstream tasks, we observe task-depen

Research Question

What is the correlation between repeated-training costs and score stability when scaling generative recommendation backbones across different user behavior sequence lengths?

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