A new decoding framework, Early-Bird (EB), is proposed to accelerate diffusion large language models (dLLMs) by adaptively grouping tokens with similar uncertainty into variable-length blocks and learning to unmask tokens in parallel. This approach achieves 3.53-18.76x higher throughput than the vanilla decoding method and is deployable as a plug-in with negligible training and inference overhead.