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Parallel Language Models via Variational Decoding

Felix Draxler, Robin C. Geyer, Daniel Geyfman, Farrin Marouf Sofian, Justus C. Will, Philipp Hildebrandt, Giosue Migliorini, Prakhar Srivastava, Gagan Sai Trinadh Duggaraju Peddiraju, Kushagra Pandey, Jorge da Silva Gonçalves, ELIJAH TAVARES, Jan Niklas Groeneveld, Yang Meng, Julia Elisabeth Vogt, Stephan Mandt · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26693 · cited by 0 works (OpenAlex) · open-access PDF

German

Autoregressive Sprachmodelle emittieren ein Token pro Vorwärtspass. Dies verursacht eine hohe Inferenzlatenz, insbesondere wenn Modelle für Qualität skaliert werden. In diesem Papier führen wir Parallel Language Models (PLMs) ein, die in einem Vorwärtspass gegenseitig konsistente Token erzeugen, indem sie Abhängigkeiten über latente Hilfsvariablen ausleiten. PLMs werden mit einer Variationslinie ausgebildet, die auf einen autoregressiven Verlust und einen parallelen Entschlüsselungsverlust reduziert. Empirisch gesehen befinden sich PLMs an einer schnelleren und besseren Pareto-Grenze als autoregressive und diskrete Diffusionsgrundlinien. Pretrained PLMs erzielen nahezu autoregressive Verluste auf

Machine translation (Meta M2M100); the English original follows.

Abstract

Autoregressive language models emit one token per forward pass. This causes high inference latency, in particular as models are scaled up for quality. In this paper, we introduce Parallel Language Models (PLMs), which generate mutually consistent tokens in one forward pass by routing dependencies through latent auxiliary variables. PLMs are trained using a variational bound that reduces to an autoregressive loss plus a parallel decoding loss. Empirically, PLMs are on a faster and better Pareto frontier than autoregressive and discrete diffusion baselines. Pretrained PLMs achieve near-autoregressive pretraining loss on model scales of 80M to 3B parameters, and speedup follows a power law in model size. When post-trained, our 1.3B model matches or exceeds an identically trained autoregressive model on math, coding, and instruction-following benchmarks, speeding up inference $2.3\text{-}5.7\times$. Together, we show that parallel generation can be trained from a single training objective at autoregressive quality. We will make our code available at https://github.com/mandt-lab/plm.

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Topics: Autoregressive model, Language model, Computer science, Inference, Decoding methods, Algorithm

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