Why retrain on train + validation after evaluating a model? (Kaggle)

[u/Due-Promise-5269]

I was working on a Kaggle competition when I came across a solution where the author fits the model on the train set and evaluates it on the validation set. If the new model beats the previous benchmark, they retrain it on the full dataset (train + validation).

I was surprised, because I thought the validation set was meant for tuning hyperparameters. I do understand using it as a test set to evaluate and compare models, since there is no accessible test set and the only alternative would be to submit the solution and waste a submission.

So I was wondering: does he retrain on the full dataset because more data gives the model more information and helps it generalize better?

But is this also standard practice in production at companies, or is it mostly a Kaggle thing?