Hierarchical Verification of Speculative Beams for Accelerating LLM Inference

2025-07-29

AI Summary

To address the inefficiency of autoregressive decoding in large language models (LLMs) and the redundant computation arising from priority-agnostic candidate verification in conventional speculative decoding, this paper proposes a hierarchical verification tree framework. Built upon beam sampling, the method constructs a probability-driven verification-and-pruning structure that prioritizes high-confidence speculative sequences for verification while dynamically pruning low-probability branches—requiring no model retraining or architectural modifications. The algorithm formally defines verification ordering and pruning criteria, enabling seamless integration into standard inference pipelines. Experiments across multiple LLMs and benchmark datasets demonstrate that the proposed approach achieves an average 27.4% reduction in inference latency and 23.8% reduction in energy consumption compared to state-of-the-art speculative decoding methods, while preserving or even improving generation quality—as measured by BLEU and ROUGE scores.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language models (LLMs) have achieved remarkable success across diverse natural language processing tasks but face persistent challenges in inference efficiency due to their autoregressive nature. While speculative decoding and beam sampling offer notable improvements, traditional methods verify draft sequences sequentially without prioritization, leading to unnecessary computational overhead. This work proposes the Hierarchical Verification Tree (HVT), a novel framework that restructures speculative beam decoding by prioritizing high-likelihood drafts and enabling early pruning of suboptimal candidates. Theoretical foundations and a formal verification-pruning algorithm are developed to ensure correctness and efficiency. Integration with standard LLM inference pipelines is achieved without requiring retraining or architecture modification. Experimental evaluations across multiple datasets and models demonstrate that HVT consistently outperforms existing speculative decoding schemes, achieving substantial reductions in inference time and energy consumption while maintaining or enhancing output quality. The findings highlight the potential of hierarchical verification strategies as a new direction for accelerating large language model inference.

Problem

• Improves LLM inference efficiency via hierarchical verification
• Reduces computational overhead in speculative beam decoding
• Maintains output quality while accelerating inference speed

Innovation

• Hierarchical Verification Tree prioritizes high-likelihood drafts
• Early pruning of suboptimal candidates reduces overhead
• Integration without retraining maintains output quality