 and adds everything together. A transformer-based language model would normally tokenize the input sequence, embed, encode, decode, de-embed then detokenize. However, the current 129&nbsp;×&nbsp;256 sequence is arguably already embedded and encoded into a continuous space of dense chess knowledge. It is perhaps missing some details specific to the value and policy heads but contains all of the building blocks. Therefore, the input embedding and encoding portion is omitted. Experiments ratified the decisio