Abstract
Block-wise diffusion large language models (dLLMs) decode sequentially at the block level, enabling effective KV-cache reuse across blocks but making inter-block decoding strictly serial. Prior work has attempted to unlock inter-block parallelism through post-training methods, but achieves only modest speedups and often degrades accuracy. We observe that self-correcting dLLMs offer a training-free alternative: token-to-token (T2T) editing can repair tokens drafted with a slightly stale upstream context, so a downstream block requires only an informative draft rather than a finalized predecessor. This turns block finality from a hard dependency into a scheduling resource.
We propose FlowBlock, a training-free parallel decoding framework built on two mechanisms. (i) Gated Wavefront Decoding admits blocks into a bounded wavefront only when a readiness gate is satisfied, jointly refines active blocks via T2T editing, and commits blocks in order under a windowed block-causal mask that preserves exact frozen-prefix KV caches reuse. (ii) Heterogeneous Wavefront Packing assigns each request an independent wavefront while packing asynchronous windows into dense, shape-stable batched forwards.
Across different benchmarks, FlowBlock improves tokens per second (TPS) over LLaDA-2.1 and LLaDA-2.0, two serial block-wise dLLMs, by up to 2.95$\times$ and 4.01$\times$, while reducing latency by up to 53.6\% and 77.1\%, respectively. It also improves average accuracy by 1.3 points. Compared with D2F, a training-based inter-block-parallel baseline, FlowBlock achieves higher accuracy and up to 16$\times$ higher batched serving throughput.
Blogger's Review: The introduction of FlowBlock marks a significant breakthrough in the field of diffusion language models. Achieving parallel decoding without training not only speeds up processing but also maintains model accuracy, showcasing a new direction for the future of language models. Its innovative T2T editing mechanism offers potential for rapid adaptation to contextual changes, warranting further research and application.