Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training
Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior. In practice, a single pretrained foundation model is often adapted to diverse downstream serving surfaces through Supervised Fine-Tuning(SFT). However, optimizing task-specific objectives such as clicks or likes does not necessarily align the serving policy with the business metrics that determine recommendation quality. We propose a three-phase progressive post-training framework tha
Record details
Published: 7 August 2026
Source: arXiv
Category: Research
Topics: Regulation · Safety & alignment
Retrieved: 10 August 2026
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ethics.ai (7 August 2026), “Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training,” evidence record 17825, https://ethics.ai/record/17825 (originally published by arXiv).
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