In Backward stepwise regression, if an independent variable is removed at any step then:

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That independent variable cannot be added back to the model at a later stage

That independent variable can be added back to the model at a later stage.

That independent variable can be added back to the model after evaluating the statistical significance of its estimated coefficient.

That independent variable can be added back to the model using K fold Cross Validation.

An advantage of Forward stepwise regression over Best subset regression is:

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Forward stepwise regression is easier to understand / explain than Best subset regression.

When the number of independent variables q is large, Forward stepwise regression is computationally more efficient than Best subset regression.

When the number of independent variables q is large, Best subset regression is computationally more efficient than Forward stepwise regression.

Forward stepwise regression is statistically more accurate than Best subset regression.