Parallel Token Prediction for Language Models (2512.21323v1)

Abstract: We propose Parallel Token Prediction (PTP), a universal framework for parallel sequence generation in LLMs. PTP jointly predicts multiple dependent tokens in a single transformer call by incorporating the sampling procedure into the model. This reduces the latency bottleneck of autoregressive decoding, and avoids the restrictive independence assumptions common in existing multi-token prediction methods. We prove that PTP can represent arbitrary autoregressive sequence distributions. PTP is trained either by distilling an existing model or through inverse autoregressive training without a teacher. Experimentally, we achieve state-of-the-art speculative decoding performance on Vicuna-7B by accepting over four tokens per step on Spec-Bench. The universality of our framework indicates that parallel generation of long sequences is feasible without loss of modeling power.

• The paper introduces PTP, a framework that jointly predicts multiple tokens in parallel while retaining the full expressive power of autoregressive models.
• The paper employs training strategies such as distillation and inverse autoregressive training, achieving over four tokens per step on the Spec-Bench benchmark with Vicuna-7B.
• The paper demonstrates that PTP overcomes the sequential bottleneck, paving the way for efficient real-time applications and multimodal generation tasks.

Parallel Token Prediction for LLMs

Introduction

The paper "Parallel Token Prediction for LLMs" (2512.21323) presents a novel framework, referred to as Parallel Token Prediction (PTP), designed to enhance sequence generation capabilities in LLMs. Traditional autoregressive models, such as transformers, generate text sequentially, imposing a latency bottleneck due to the dependency of each token on its predecessors. The proposed PTP framework addresses this limitation by jointly predicting multiple interdependent tokens within a single transformer call, bypassing the restrictive assumptions of independence in current multi-token prediction methods.

Framework and Theoretical Foundation

The authors introduce PTP as a framework capable of representing arbitrary autoregressive sequence distributions, a claim supported by proofs demonstrating its expressiveness equivalence to traditional autoregressive models. The PTP framework integrates the concept of sampling auxiliary random variables into the model's architecture. This deterministic embedding of sampling procedures allows the model to predict which tokens will be generated, thus decoupling the inherent sequential dependency present in existing methods.

PTP employs two primary training strategies: distillation of an existing model and inverse autoregressive training without a teacher. Through these methods, PTP maintains the expressive power necessary for complex language tasks while allowing faster and more efficient parallel generation of sequences.

Experimental Results

The experimental evaluations demonstrate that PTP achieves state-of-the-art results, particularly in speculative decoding scenarios. For instance, the authors report achieving over four tokens per step on the Spec-Bench benchmark using Vicuna-7B, highlighting the framework's potential to significantly boost throughput in LLM applications.

Moreover, a comparison with alternative multi-token prediction and decoding systems illustrates PTP's superiority in generating coherent sequences by leveraging auxiliary information. This strategic use of auxiliary variables increases the number of correctly predicted tokens and reduces latency, setting a new benchmark for speculative decoding performance.

Practical Implications and Future Work

The research introduces a versatile design space allowing for the construction of models capable of generating longer sequences in parallel without sacrificing predictive accuracy. PTP's ability to operate without the typical independence assumptions positions it as a significant advancement in the field of NLP and machine learning.

Looking forward, the paper suggests several avenues for future exploration. The adoption of PTP in larger-scale models and its integration with multimodal generation tasks—such as those involving both text and visual data—hold promise for further enhancing LLM performance. Additionally, combining PTP with other acceleration strategies could lead to even greater efficiencies.

The theoretical foundations laid by the framework suggest that the bottleneck associated with sequential generation in autoregressive models is not an immutable constraint. This recognition paves the way for developing truly universal, efficient parallel generation techniques that are well-suited for a wide array of applications, from real-time conversational agents to large-scale data generation tasks.

Conclusion

The Parallel Token Prediction framework represents a substantial step forward in LLM development, providing a principled approach to parallelizing sequence generation. By preserving the modeling power of traditional methods while enhancing speed and reducing latency, PTP offers a robust solution to some of the longstanding challenges in the field of NLP. As LLMs continue to grow in complexity and application scope, innovations like PTP will be critical to their success and practicality.