English(EN) Progressive Alignment of Recommender Foundation Model through Multi-Phase Post-Training

新框架将推荐基础模型与业务指标对齐 · 跟踪 2 个来源

作者 PulseAugur 编辑部 · [2 个来源] · 2026-08-07 04:30

研究人员开发了一种新颖的三阶段后训练框架，以更好地将推荐基础模型与业务指标对齐。这种渐进式方法将下游适应（使用线性探测和完全微调）与通过带有学习奖励模型的强化微调进行的业务指标对齐分开。实验表明，这种多阶段方法优于单阶段替代方案，并在大规模在线测试中提高了推荐质量。 AI

影响这项研究可能导致生产环境中更有效且与业务对齐的推荐系统。

排序理由该集群包含一篇详细介绍人工智能模型训练新方法的论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Oseong Choi, Hoeinn Kim, Jihoon Lee, Byungsoo Kang, Taeyeong Jang · 2026-08-10 04:00

通过多阶段后训练实现推荐基础模型的渐进式对齐

arXiv:2608.06792v1 Announce Type: cross Abstract: 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 Sup…
arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Taeyeong Jang · 2026-08-07 04:30

通过多阶段后训练实现推荐基础模型的渐进式对齐

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…