BPE-dropout is applied at which stage(s) of the LLM lifecycle?

BPE normally tokenizes a word the same way every time, because it applies its learned merges greedily until none are left. **BPE-dropout** changes that during model training: at each merge step it skips the merge with some probability p. The result is that the same word can be split into different subword sequences on different training passes. This is a form of data augmentation, like randomly cropping images. The language model sees 'tokenization' as one token sometimes, and as 'token', 'ization' other times. It learns that the meaning is the same regardless of how the pieces are cut. At inference time you switch the randomness off and tokenize deterministically. Users expect the same prompt to give the same tokens, so you do not want dice rolls deciding segmentation when someone is actually using the model. So the correct answer is training time only.

The answer is **training time only**, and specifically model-training time. BPE-dropout is subword regularization. During training, each merge in the BPE sequence is dropped with probability p. A single word therefore gets many possible segmentations across epochs. $$P(\text{skip a given merge}) = p$$ That variability acts like augmentation. The model learns robust representations because it cannot rely on one canonical split, which helps especially on rare words, noisy text, and morphologically rich languages. At inference you set p to 0 and tokenize deterministically. Two reasons. First, users expect the same prompt to produce the same tokens and outputs. Second, the regularization benefit is purely a training-time effect; randomizing segmentation at serve time only adds uncontrolled noise on top of temperature sampling. The common trap is option D: tokenizer-training time. BPE-dropout does not change how the vocabulary is learned. The vocabulary and merge rules are fixed; dropout perturbs how that fixed rule set is applied while the model trains.