I need some help to understand the relationship between the ranking of the variables from the LARS algorithm and the use of OLS to estimate the final model chosen by the LARS.

I understand that the LARS algorithm is less greedy than forward stepwise regression because it does not require additional predictors to be orthogonal to the residual and the already included predictor. But after the LARS has ranked the variables and chosen the optimal number of predictors to include in the model, we use OLS to estimate the model. The OLS parameters are different from those assigned to the predictors in the LARS, right? So how can I intuitively explain why it is correct to first use LARS and then OLS on the model selected by LARS?