DLoop proposes a looped speculative decoding scheme that eliminates the verification step after each drafting stage when the target model accepts all proposed tokens, allowing the draft model to continue generating tokens uninterrupted. By reducing unnecessary target-model forward passes, the method improves inference speed for large language models while preserving correctness. The approach leverages increasingly capable draft models to maximize acceptance rates.