Speeding Up Language Models: A New Approach to Token Generation

Author: Denis Avetisyan

Researchers have developed a novel framework that dramatically reduces the time it takes for large language models to generate text.

Parallel token prediction streamlines sequence generation by enabling a single model call to predict multiple tokens simultaneously, achieved through either integrating sampling directly into the model-using auxiliary variables-or by modeling and predicting the distribution of each token in parallel with those same variables, a departure from traditional autoregressive models that predict tokens one at a time <span class="katex-eq" data-katex-display="false"> t_{i} </span>. — Parallel token prediction streamlines sequence generation by enabling a single model call to predict multiple tokens simultaneously, achieved through either integrating sampling directly into the model-using auxiliary variables-or by modeling and predicting the distribution of each token in parallel with those same variables, a departure from traditional autoregressive models that predict tokens one at a time $t_{i}$ .

Parallel Token Prediction enables simultaneous generation of multiple tokens, offering a compelling alternative to traditional autoregressive decoding.

Autoregressive language models, while powerful, are inherently limited by sequential decoding, creating a latency bottleneck for real-time applications. This paper introduces ‘Parallel Token Prediction for Language Models’, a universal framework enabling the simultaneous prediction of multiple dependent tokens within a single transformer pass. By integrating sampling directly into the model, PTP achieves significant speedups without compromising representational capacity-demonstrating state-of-the-art speculative decoding performance. Could this approach unlock a new era of efficient and scalable language generation for demanding applications?

The Inevitable Bottleneck: Why Speed Matters (and Isn’t Getting Faster)

Autoregressive Transformers, despite demonstrating remarkable capabilities in natural language processing, are fundamentally limited by their sequential decoding process. Each token generated depends on all preceding tokens, forcing the model to compute one token at a time – a critical bottleneck in inference speed. This isn’t a matter of computational power, but an architectural constraint; even with increased processing units, the inherent serial nature of the process remains. Consequently, generating longer sequences becomes proportionally slower, impacting real-time applications like interactive dialogue systems and hindering the deployment of ever-larger models. The time required to generate a sequence isn’t simply additive – the dependency chain introduces a compounding effect, making efficient parallelization extremely challenging and driving the need for innovative decoding strategies.

The inherent sequential processing of autoregressive Transformers introduces a significant latency bottleneck during inference. Each token generated is dependent on all preceding tokens, forcing the model to compute step-by-step rather than in parallel. This directly translates to increased $Inference\,Latency$ , posing challenges for real-time applications such as conversational AI and interactive systems where immediate responses are crucial. Moreover, as models grow in $Model\,Capacity$ -increasing the number of parameters to improve performance-the computational burden of this sequential decoding amplifies, making it increasingly difficult and expensive to scale these powerful models to handle complex tasks and larger input sequences without unacceptable delays.

Despite continual advancements in model size and parameter count, simply increasing the capacity of autoregressive Transformers fails to address the fundamental limitation of sequential decoding. While larger models can capture more complex relationships within data, they remain constrained by the need to generate output one token at a time. This inherently limits processing speed, as each subsequent token is dependent on all previously generated tokens. Consequently, gains from increased model capacity are often offset by the escalating computational cost of sequential processing, preventing true scalability and hindering deployment in applications demanding low latency. A genuine breakthrough, therefore, necessitates a paradigm shift – moving beyond simply building larger models to developing entirely new decoding strategies that allow for parallelization and overcome the sequential bottleneck.

Parallel Token Prediction (PTP) enables draft models to achieve significant speedups over standard autoregressive decoding-by generating multiple tokens per step-and demonstrates increasing parallelism with model size, as shown by the improved speed and token acceptance rates across varying parameter counts.

Breaking the Chain: A Parallel Approach (If It Works)

Parallel Token Prediction (PTP) represents a departure from the standard autoregressive decoding process employed by Transformers, which generates output tokens one at a time, sequentially. Instead of conditioning each new token prediction solely on previously generated tokens, PTP aims to predict multiple tokens concurrently within a single forward pass. This is achieved by restructuring the decoding process to enable simultaneous computation of token probabilities, fundamentally altering the linear dependency inherent in sequential decoding and opening the possibility for significant reductions in inference time. The core principle is to increase throughput by processing multiple decoding steps in parallel, rather than serially.

Parallel Token Prediction (PTP) utilizes auxiliary variables to enable simultaneous token prediction by providing each potential token with necessary contextual information. These variables, computed alongside the standard autoregressive pass, act as conditional inputs for independent prediction heads – one for each token position. Specifically, the auxiliary variables encapsulate information from previously processed tokens, allowing each prediction head to operate with sufficient context without requiring sequential dependency. This contrasts with standard autoregressive models where each token’s prediction is conditioned on all preceding tokens, creating an inherent bottleneck for parallelization. The use of auxiliary variables, therefore, facilitates a departure from strict sequential decoding by providing the necessary contextual dependencies for parallel token generation.

Parallel Token Prediction (PTP) builds upon the foundation of Autoregressive Transformers by enabling the simultaneous prediction of multiple output tokens, a departure from the inherently sequential nature of standard autoregressive models. This is achieved without compromising the established strengths of the Transformer architecture, such as its attention mechanisms and ability to model long-range dependencies. By predicting tokens in parallel, PTP directly addresses the primary bottleneck in Transformer inference – the sequential dependency between token generations – thereby reducing overall inference latency. The reduction in latency is proportional to the degree of parallelism achieved, offering a scalable pathway to faster and more efficient text generation and other sequence-to-sequence tasks.

Parallel token prediction successfully generates meaningful code token pairs by coordinating predictions through auxiliary variables, achieving sensible outputs in nearly all cases, unlike autoregressive or independent prediction methods which frequently produce incompatible combinations.

Knowledge Transfer: Teaching the Student (And Hoping It Learns)

One-Hot Probabilistic Teacher Forcing (PTP) is a knowledge distillation technique where a pre-trained Teacher Model guides the learning process of a Student Model. During training, the Student Model generates parallel predictions, and these are then evaluated against the Teacher Model’s output via Sequence Verification. This verification process involves comparing the Student’s predicted sequence against the Teacher’s, providing a signal to refine the Student’s parameters. Essentially, the Teacher Model acts as a supervisor, providing correct sequence information to encourage the Student to align its predictions with established, high-quality outputs, effectively transferring knowledge from the larger, potentially more complex Teacher to the Student.

Categorical Probability Token Prediction (PTP) represents an advancement over one-hot PTP by moving beyond predicting only the most likely token at each step. Instead, it models the full probability distribution across the entire vocabulary for each token position. This allows the model to generate a probability distribution over all possible tokens, facilitating self-training without requiring a pre-trained teacher model. The model learns by comparing its predicted token distributions to the actual observed tokens in the training data, optimizing its parameters to better align with the empirical distribution. This self-supervised approach enhances model performance and eliminates the need for external knowledge transfer from a teacher network.

Inverse Autoregressive Training (IAT) builds upon Categorical Prediction Tokenization (PTP) by removing the requirement for a teacher model and enabling direct learning from unlabeled data. This is achieved by framing the task as predicting subsequent tokens given a context, effectively utilizing the model’s own output distribution as a training signal. During IAT, the model predicts the probability distribution over the next token, and this distribution is then used to sample a token, which is appended to the context. The process repeats iteratively, creating a self-supervised training loop where the model learns to generate coherent sequences without external guidance. This method improves performance by leveraging the full potential of unlabeled data and reducing the reliance on potentially biased or limited teacher models.

A parallelized model efficiently replicates the output of an autoregressive teacher model-completing an entire function while the teacher only generates its signature-as demonstrated by generating a factorial function, where green indicates correct tokens, red denotes errors, and semitransparent tokens represent rejected attempts after an initial mistake.

The Cost of Progress: Efficiency at What Price?

Gated LoRA represents a significant advancement in knowledge transfer from large, pre-trained ‘Teacher Models’ to smaller, more manageable counterparts. This technique avoids the computationally expensive process of updating all the parameters of a large model during distillation; instead, it introduces a small number of trainable parameters – ‘gates’ – that control the flow of information from the teacher. By focusing adaptation on these gates, Gated LoRA dramatically reduces the number of parameters needing adjustment, enabling efficient fine-tuning with minimal computational resources. The result is a streamlined distillation process that preserves the knowledge of the larger model while creating a smaller, faster, and more readily deployable model – all without sacrificing performance.

The combination of Parameter-Efficient Tuning (PTP) and Low-Rank Adaptation (LoRA) presents a compelling pathway to diminish the computational burden associated with large language models. Traditional deployment often requires substantial resources due to the sheer size of these models, making real-time applications and wider accessibility challenging. However, by strategically reducing the number of parameters actively involved during inference-essentially operating with a streamlined, adapted version of the original model-PTP and LoRA dramatically lower both memory requirements and processing demands. This optimization not only accelerates inference speeds but also unlocks the possibility of deploying powerful language capabilities on hardware with limited resources, fostering innovation and broadening the scope of potential applications for these advanced technologies.

The convergence of parameter-efficient tuning methods like PTP and LoRA promises a future where large language models are no longer confined to resource-rich environments. By dramatically reducing computational demands during both training and inference, these techniques democratize access, enabling deployment on less powerful hardware and fostering wider adoption across diverse applications. This shift isn’t merely about speed; it’s about sustainability, lessening the considerable energy footprint associated with continually running massive models. The potential extends to real-time applications, personalized AI assistants, and on-device processing, ultimately paving the way for a more inclusive and environmentally responsible landscape for artificial intelligence.

Looking Ahead: Speculation and Parallelism

Speculative decoding represents a significant advancement in accelerating language model inference, building directly upon the principles established by Parallel Token Processing (PTP). This technique introduces a smaller, faster “draft model” that proactively proposes potential tokens for the main model to evaluate. Rather than waiting for each token to be generated sequentially, the draft model anticipates future outputs, allowing the primary model to focus on verifying or correcting these proposals. This parallel processing drastically reduces the time spent in the decoding phase, as the main model isn’t solely responsible for generating each token from scratch. The efficiency stems from the draft model’s ability to quickly pre-compute likely candidates, effectively creating a pipeline where token proposals are readily available, resulting in substantially lower inference latency-particularly beneficial for real-time applications demanding rapid responses.

The pursuit of faster artificial intelligence processing has led to a compelling combination of techniques focused on minimizing inference latency – the time it takes for a model to generate an output. By integrating Probability of Token Prediction (PTP) with speculative decoding, a powerful synergy emerges. Speculative decoding leverages a smaller, faster ‘draft’ model to propose potential tokens, which are then verified by the larger, more accurate primary model. PTP enhances this process by providing a probability distribution over possible tokens, guiding the draft model towards more likely candidates and significantly reducing the verification burden on the primary model. This collaborative approach allows for parallel processing and minimizes sequential bottlenecks, resulting in substantial reductions in processing time and enabling real-time applications that demand rapid responses, such as live translation or interactive virtual assistants.

Recent advancements in parallel token processing (PTP) have yielded a model capable of accepting up to sixteen tokens per step, a significant leap in efficiency particularly noticeable in a 1.1 billion parameter configuration. This performance isn’t merely theoretical; rigorous testing on the SpecBench benchmark demonstrates state-of-the-art results, consistently outperforming established baseline models. Importantly, this heightened efficiency isn’t limited to smaller models; performance remains remarkably consistent and scalable, extending to larger configurations with up to 7 billion parameters, suggesting a robust and adaptable architecture for real-time applications demanding rapid inference speeds.

The pursuit of speed in large language models feels perpetually Sisyphean. This paper’s Parallel Token Prediction attempts to break the sequential bottleneck, aiming for parallel decoding without losing fidelity – a noble goal, certainly. However, one suspects that any gains achieved will quickly be consumed by the ever-increasing demands of ‘progress’. As Henri Poincaré observed, ‘mathematics is the art of giving reasons.’ Perhaps, but applied to engineering, it’s often the art of justifying increasingly complex solutions to problems created by previous ‘optimizations’. The core concept of accelerating inference through parallelization isn’t new, it’s simply the latest iteration in a long line of attempts to outrun the inevitable – production traffic. Better one well-understood sequential process than a hundred parallel ones each with its own subtle failure mode.

What’s Next?

The introduction of Parallel Token Prediction (PTP) feels… predictably optimistic. A framework to sidestep the inherent sequentiality of autoregressive transformers. It’s a clever bit of engineering, certainly, but one suspects production will have its say. The claim of reduced latency without representational loss is a gauntlet thrown down to the gods of edge cases and adversarial inputs. Expect a flurry of papers attempting to break PTP – and, inevitably, succeeding in at least a few amusing ways.

The real challenge isn’t simply speed; it’s the illusion of intelligence. PTP, like discrete diffusion and normalizing flows before it, addresses a symptom, not the disease. The underlying problem remains: large language models are, at their core, glorified pattern-matching machines. Parallelism doesn’t change that. It merely allows the machine to match patterns faster.

One anticipates a future filled with increasingly baroque attempts to parallelize the impossible. Each innovation will bring temporary gains, followed by the inevitable discovery of some new, frustrating constraint. Everything new is old again, just renamed and still broken. The cycle continues. Perhaps, eventually, someone will remember that sometimes, slow and deliberate is preferable to fast and wrong.

Original article: https://arxiv.org/pdf/2512.21323.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/