Autoregressive generation can self-reinforce: once a phrase like "This means... This means..." appears in the output, that repeated text becomes part of the context and makes a similar continuation more likely. Fixes include a repetition penalty in decoding, explicit stop sequences, clearer prompt scope, lower max tokens, and instruction tuning aimed at conciseness.

Since the model generates one token at a time and each generated token becomes part of the context for the next prediction, previously generated text can reinforce similar next tokens — leading to loops of near-identical phrasing. Repetition also shows up when the model is trying to be comprehensive but has run out of genuinely new information, so it rephrases the same idea instead. Practical mitigations: a repetition penalty down-weights tokens that already appeared in the output; stop sequences halt generation at a known completion pattern; clearer prompts with explicit scope and length expectations reduce the ambiguity that leads to padding; a lower max token budget prevents the model from filling space once it's out of content; adjusted sampling (top-p/top-k or temperature) can break a repetitive rut; and models fine-tuned for conciseness are less prone to filler loops in the first place. In production, repetition is usually a symptom of either a weak/underspecified prompt or the model genuinely running out of new content to say.