A one-step lookahead stopping rule based on expected improvement per cost is a practical, theoretically-grounded alternative to fixed-budget training that adapts to your cost constraints.
This paper studies when to stop training machine learning models during hyperparameter optimization. Instead of training until a fixed budget, the authors propose EI stopping: keep training the most promising candidate until the expected improvement from one more training step doesn't justify its cost.