Parallel Loop Transformer for Efficient Test-Time Computation Scaling

2025-10-28

AI Summary

To address high inference latency, substantial memory overhead, and strong sequential dependencies inherent in conventional recurrent Transformers for large language models (LLMs), this paper proposes the Parallel Loop Transformer (PLT). Its core innovation is the first introduction of Cross-Loop Parallelism (CLP), which eliminates temporal dependencies across loops. PLT further integrates shared key-value (KV) caching, gated sliding-window attention (G-SWA), and efficient representation enhancement to jointly optimize computational efficiency, memory footprint, and accuracy. Experiments demonstrate that PLT achieves accuracy comparable to full-loop baselines while significantly reducing end-to-end latency and GPU memory consumption: inference speed improves by up to 2.1×, and KV cache memory usage decreases by 37%. These results establish PLT as a novel paradigm for low-latency, cost-effective LLM deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large Language Models (LLMs) are powerful but often too slow and costly for real-world use during inference. Looped transformers save on parameters by reusing the same weights for multiple computational steps, or "loops." However, this approach has a major flaw: the loops run one after another, causing inference latency and memory requirements to increase with each added loop. This makes them impractical for fast applications. To solve this problem, we introduce the Parallel Loop Transformer (PLT). PLT is a new architecture that delivers the performance benefits of a deep, looped model but with the low latency of a standard, non-looped model. PLT works using two key techniques. First, Cross-Loop Parallelism (CLP) breaks the sequential dependency by computing different loops for different tokens at the same time, all within a single pass. Second, to prevent memory costs from growing, we use an Efficient Representation Enhancement strategy. This method shares the memory (KV cache) from the first loop with all other loops. It then uses a Gated Sliding-Window Attention (G-SWA) to combine this shared global information with local information, maintaining high accuracy. Our experiments show that PLT achieves the high accuracy of a traditional looped model but with almost no extra latency or memory cost compared to a standard transformer.

Problem

• Reduces inference latency in looped transformer architectures
• Minimizes memory costs while maintaining model accuracy
• Enables parallel computation of loops for different tokens

Innovation

• Parallel Loop Transformer enables simultaneous token processing
• Cross-Loop Parallelism breaks sequential dependency in loops
• Gated Sliding-Window Attention combines global and local information