Set Block Decoding Is a Language Model Inference Accelerator (arxiv.org)

Researchers have introduced Set Block Decoding (SBD), a novel approach to accelerating language model inference by enabling parallel generation of multiple, potentially non-consecutive tokens during decoding. Unlike traditional autoregressive models that predict tokens strictly one-by-one, SBD combines standard next token prediction with masked token prediction within a single framework. This innovation leverages techniques from discrete diffusion models to achieve faster generation without compromising output quality. SBD stands out by requiring no changes to existing model architectures or training hyperparameters, maintaining full compatibility with key-value caching strategies widely used for efficient inference. Implemented through fine-tuning of popular large language models like Llama-3.1 8B and Qwen-3 8B, SBD demonstrated a 3 to 5-fold reduction in the number of forward passes needed, effectively speeding up generation while retaining comparable performance to conventional next token prediction methods. For the AI and ML community, this development has significant practical implications: it addresses one of the main bottlenecks in deploying large autoregressive models—slow, expensive token-by-token decoding—by offering a flexible, adaptable acceleration technique that can be integrated with existing models to boost efficiency and reduce inference costs.