Each tokenizer has its own way of representing pieces of the same word, because the model expact them in different ways. For instance Bert model expects BPE tokenization because it was pretrained this way. GPT-2 and RoBERTa expect byet-level BPE (because they were pretrained this way) which results in outputs with those Ġ (which basically represent space) whereas XLNet expects sentencepiece tokenized texts, which uses ▁ to represent the space character.
You can find a high-level summary of the tokenizers and which one is used for each model in this doc page 31.