We consider the linear regression model along with the process of its $α$-regression quantile, $0<α<1$. We are interested mainly in the slope components of $α$-regression quantile and in their dependence on the choice of $α.$ While they are invariant to the location, and only the intercept part of the $α$-regression quantile estimates the quantile $F^{-1}(α)$ of the model errors, their dispersion depends on $α$ and is infinitely increasing as $α\rightarrow 0,1$, in the same rate as for the ordinary quantiles. We study the process of $R$-estimators of the slope parameters over $α\in[0,1]$, generated by the Hájek rank scores. We show that this process, standardized by $f(F ^{-1}(α))$ under exponentially tailed $F$, converges to the vector of independent Brownian bridges. The same course is true for the process of the slope components of $α$-regression quantile.