Active error correction for Abelian and non-Abelian anyons

We consider a class of decoding algorithms that are applicable to error correction for both Abelian and non-Abelian anyons. This class includes multiple algorithms that have recently attracted attention, including the Bravyi-Haah RG decoder and variants thereof. They are applied to both the problem of correcting a single burst of errors (with perfect syndrome measurements) and active correction of continuously occurring errors (with noisy syndrome measurements). For Abelian models we provide a threshold proof in both cases for all decoders in this class, showing that they can arbitrarily suppress errors when the noise rate is under a finite threshold. For non-Abelian models such a proof is found for a single burst of errors. The reasons why the proof cannot be applied to the case of continuously occurring errors are discussed.