👉 Adjustment math, often used in machine learning and optimization, is a method for smoothly transitioning between different models or parameter sets, typically to improve stability and performance during training. Instead of abruptly switching from one model to another, which can cause abrupt changes in the optimization landscape and lead to poor convergence or instability, adjustment math gradually modifies the parameters of the current model based on the parameters of a target model. This is usually achieved by defining an adjustment function that maps the parameters of the current model to those of the target model, and then updating the current model's parameters using a weighted combination of the old and new parameters. The weights are typically determined by a learning rate schedule, which controls how quickly the model adapts to the target parameters. This gradual adjustment helps in fine-tuning the model's performance and can lead to better generalization and faster convergence.
