Parallel Decoding

Parallel decoding reduces sequential generation by committing multiple positions together, drafting and verifying token blocks, or processing independent regions concurrently; it matters when inference latency is costly. Diffusion language models use confidence-, attention-, or dependency-aware unmasking, while speculative methods use parallel drafts, trees, or block repair checked by a target model; structured tasks can exploit schemas, layouts, and weakly dependent components. These approaches can improve throughput, but their gains depend on hardware, batching, cache use, and task structure. The central tension is that individually predictable tokens may still be correlated, so aggressive parallelism can hurt coherence or exactness unless methods estimate dependencies or verify and correct proposals without adding too much overhead.