  Despite the extreme error-correction performance, the amount of computation
of sequential decoding of the polarization-adjusted convolutional (PAC) codes
is random. In sequential decoding of convolutional codes, the computational
cutoff rate denotes the region between rates whose average computational
complexity of decoding is finite and those which is infinite. In this paper, by
benefiting from the polarization and guessing techniques, we prove that the
computational cutoff rate in sequential decoding of pre-transformed polar codes
polarizes. The polarization of the computational cutoff rate affects the
criteria for the rate-profile construction of the pre-transformed polar codes.
We propose a technique for taming the Reed-Muller (RM) rate-profile
construction, and the performance results demonstrate that the error-correction
performance of the PAC codes can achieve the theoretical bounds using the
tamed-RM rate-profile construction and requires a significantly lower
computational complexity than the RM rate-profile construction.
