Diffusion language models (DLMs) are theoretically efficient but are limited by fixed-length decoding and poor compatibility with KV caches. Block diffusion mitigates these issues, yet still enforces a fixed block size and costly training. We introduce Next Sequence Prediction (NSP), which unifies next token and next block prediction, enabling the model to adaptively determine generation length at each step. NSP reduces to standard next token prediction when the length is one. Building on NSP, we propose the Sequential Diffusion Language Model (SDLM), which retrofits pretrained autoregressive language models (ALMs) at minimal cost. Specifically, SDLM performs diffusion inference within fixed-size mask blocks, but dynamically decodes contiguous subsequences based on model confidence, thereby preserving KV-cache compatibility and improving robustness to the varying uncertainty and semantics across the sequence. SDLM matches or surpasses strong autoregressive baselines with only 3.5M training samples, while achieving 2.0× 3.4× higher throughput than Qwen2.5 across three model scales (3B, 7B, 32B) with confidence-based decoding. Furthermore, when sampling with self-speculative decoding, SDLM attains up to 5.7× end-to-end speedup measured as wall-clock tokens per second (TPS). Notably, SDLM-32B achieves performance comparable to Qwen2.5-32B with up to 2.5× speedup, demonstrating the scalability of our approach. Project page and codes: https://github.com/OpenGVLab/SDLM