  The recently introduced recursive projection aggregation (RPA) decoding
method for Reed-Muller (RM) codes can achieve near-maximum likelihood (ML)
decoding performance. However, its high computational complexity makes its
implementation challenging for time- and resource-critical applications. In
this work, we present a complexity reduction technique called multi-factor
pruning that reduces the computational complexity of RPA significantly. Our
simulation results show that the proposed pruning approach with appropriately
selected factors can reduce the complexity of RPA by up to $92\%$ for
$\text{RM}(8,3)$ while keeping the comparable error-correcting performance.
