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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

Chinese

在这篇论文中,我们介绍了平行语言模型(PLMs),通过通过隐形辅助变量路由一个代币发行一个代币。这导致了高推断延迟,特别是因为模型被缩放到质量上。在本文中,我们介绍了平行语言模型(PLMs),通过通过隐形辅助变量路由,在一个前进路由中产生相互一致的代币。PLMs通过使用变量界限进行训练,从而减少到自我退缩损失加上平行解码损失。从经验上讲,PLMs处于比自我退缩和分散扩散基线更快和更好的帕雷托边界。受过训练的PLMs在80M到3B参数模型尺度上实现了近乎自我退缩的预训练损失,并且速度提升遵循

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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