Parallel Language Models: generating multiple consistent tokens per pass to cut autoregressive latency
burkov · x · 2026-11-02
Andriy Burkov highlights a new article attacking the core latency bottleneck of autoregressive LLMs — one token per forward pass. The proposed Parallel Language Models (PLMs) use auxiliary variables to coordinate multiple parallel token predictions so they stay mutually consistent, without sacrificing quality or trainability.
The training method pairs an autoregressive component that encodes text into auxiliary signals with a parallel component that reconstructs text from those signals, trained directly on text rather than distilled from an existing model. Experiments cover 80M–3B parameter models on a general text mixture, benchmarked against matched autoregressive models and public LM/diffusion baselines.
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