Byte pair encoding (BPE) or digram coding is a simple and robust form of data compression in which the most common pair of contiguous bytes of data in a sequence are replaced with a byte that does not occur within the sequence. A lookup table of the replacements is required to rebuild the original data. The algorithm was first described publicly by Philip Gage in a February 1994 article "A New Algorithm for Data Compression" in the C Users Journal.

A variant of the technique has shown to be useful in several natural language processing (NLP) domains, for applications such as tokenisation, as seen in Google's SentencePiece and OpenAI's GPT-3. Here, the goal is not data compression, but tokenisation of text in a given language to produce a variable sequence of terms from a fixed-size vocabulary of tokens. Typically, most words will be encoded as a single token, while rare words will be encoded as a sequence of a few tokens, where these tokens represent meaningful word parts. This translation of text into tokens can be found by variants of byte pair encoding, such as subword units.

Byte pair encoding lends itself to NLP tasks due to its simplicity and speed; BPE is suitably effective for the tokenisation of terms, does not require large computational overheads, and remains consistent, making it reliable.
here is a blurb about byte pair encoding for encryption, what benefits does this concept have besides compression?
An alternate use for byte pair encoding besides compression would be tokenization.  Tokenization can be used in Natural Language Processing to encode tokens of words for simplicity and speed in NLP tasks.