Machine learning models deployed in nonstationary environments experience performance degradation due to data drift. While many drift detection heuristics exist, most lack a principled dynamical interpretation and provide limited guidance on how retraining frequency should be balanced against operational cost. In this work, we propose an entropy--based retraining framework grounded in nonequilibrium stochastic dynamics. Modeling deployment--time data drift as probability flow governed by a Fokker--Planck equation, we quantify model--data mismatch using a time--evolving Kullback--Leibler divergence. We show that the time derivative of this mismatch admits an entropy--balance decomposition featuring a nonnegative entropy production term driven by probability currents. This interpretation motivates entropy--triggered retraining as a label--free intervention strategy that responds to accumulated mismatch rather than delayed performance collapse. In a controlled nonstationary classification experiment, entropy--triggered retraining achieves predictive performance comparable to high--frequency retraining while reducing retraining events by an order of magnitude relative to daily and label--based policies.

翻译：部署在非平稳环境中的机器学习模型会因数据漂移而经历性能退化。尽管存在许多漂移检测启发式方法，但大多数缺乏原理性的动力学解释，并且在如何平衡重训练频率与操作成本方面提供的指导有限。在这项工作中，我们提出了一种基于非平衡随机动力学的熵驱动重训练框架。通过将部署期间的数据漂移建模为由Fokker-Planck方程控制的概率流，我们使用时变的Kullback-Leibler散度来量化模型与数据的失配。我们证明了该失配的时间导数允许一种熵平衡分解，其包含一个由概率流驱动的非负熵产生项。这一解释将熵触发式重训练激励为一种无标签干预策略，它响应累积的失配而非延迟的性能崩溃。在一个受控的非平稳分类实验中，熵触发式重训练实现了与高频重训练相当的预测性能，同时相对于每日重训练和基于标签的策略，将重训练事件减少了一个数量级。