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arXiv · 2512.21323

Parallel Token Prediction for Language Models

Felix Draxler·Justus Will·Farrin Marouf Sofian·Theofanis Karaletsos·Sameer Singh·Stephan Mandt
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Abstract

Autoregressive decoding in language models is inherently slow, generating only one token per forward pass. We propose Parallel Token Prediction (PTP), a general-purpose framework for predicting multiple tokens in a single model call. PTP moves the source of randomness from post-hoc sampling to random input variables, making future tokens deterministic functions of those inputs and thus jointly predictable in a single forward pass. We prove that a single PTP call can represent arbitrary dependencies between tokens. PTP is trained by distilling an existing model or through inverse autoregressive training without a teacher. Experimentally, PTP achieves a 2.4x speedup on a diverse-task speculative decoding benchmark. We provide code and checkpoints at https://github.com/mandt-lab/ptp.

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Felix Draxler, Justus Will, Farrin Marouf Sofian, Theofanis Karaletsos, Sameer Singh, Stephan Mandt. 2025-12-24. Parallel Token Prediction for Language Models. https://arxiv.org/abs/2512.21323

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