重训练可视为受限计算下的近似贝叶斯推断，用决策理论优化触发时机。
Retraining as Approximate Bayesian Inference
- 将重训练视为贝叶斯推断，用损失函数导出最优重训阈值
- 提出基于证据的触发机制，替代固定时间表
- 适合关注模型治理与可审计性的研发团队
模型重训练通常被视为持续维护任务。但哈里森·卡茨认为，重训练更应被理解为在计算约束下的近似贝叶斯推断。不断更新的信念状态与冻结部署模型之间的差距称为「学习债务」，而重训练决策是一个成本最小化问题，其阈值由损失函数自然导出。本文提出了一个基于决策论的重训策略框架，实现了基于证据的触发机制，取代了日历驱动的调度，并使模型治理过程可审计。对不熟悉贝叶斯与决策论术语的读者，文章末尾附有术语表。
原文摘要 · Abstract (English)
Model retraining is usually treated as an ongoing maintenance task. But as Harrison Katz now argues, retraining can be better understood as approximate Bayesian inference under computational constraints. The gap between a continuously updated belief state and your frozen deployed model is "learning debt," and the retraining decision is a cost minimization problem with a threshold that falls out of your loss function. In this article Katz provides a decision-theoretic framework for retraining policies. The result is evidence-based triggers that replace calendar schedules and make governance auditable. For readers less familiar with the Bayesian and decision-theoretic language, key terms are defined in a glossary at the end of the article.
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