  Guessing random additive noise decoding (GRAND) is a recently proposed
decoding paradigm particularly suitable for codes with short length and high
rate. Among its variants, ordered reliability bits GRAND (ORBGRAND) exploits
soft information in a simple and effective fashion to schedule its queries,
thereby allowing efficient hardware implementation. Compared with maximum
likelihood (ML) decoding, however, ORBGRAND still exhibits noticeable
performance loss in terms of block error rate (BLER). In order to improve the
performance of ORBGRAND while still retaining its amenability to hardware
implementation, a new variant of ORBGRAND termed RS-ORBGRAND is proposed, whose
basic idea is to reshuffle the queries of ORBGRAND so that the expected number
of queries is minimized. Numerical simulations show that RS-ORBGRAND leads to
noticeable gains compared with ORBGRAND and its existing variants, and is only
0.1dB away from ML decoding, for BLER as low as $10^{-6}$.
