Autoregressive Generation

Autoregressive generation produces text one token at a time: at each step the model receives the prompt plus all previously generated tokens as context, then predicts a probability distribution over the vocabulary and samples or greedily selects the next token. This token is appended to the sequence and the process repeats until an end-of-sequence token appears or a length limit is reached. The approach is simple and produces high-quality text but scales linearly in generation cost with output length.