SpecScale speeds up large language model reasoning with smarter search
Taming Speculative Search for Test-Time Scaling in LLM Serving
Distributed, Parallel, and Cluster ComputingComputation and LanguageOperating Systems
Summary
When large language models try to solve tricky problems like math or coding, they run lots of possible solution steps to figure out the best answer. But exploring all these paths can be slow and wasteful. The authors designed SpecScale, a system that quickly cuts off bad options, reuses similar steps, and delays detailed checking. This lets the model think faster without losing accuracy, making it more useful for hard reasoning tasks.
What this means in practice
- •For ai infrastructure teams: Improve inference speed and throughput when running large language models on challenging reasoning tasks by reducing wasted computation during speculative search.
- •For cloud service operators: Deploy more efficient large language model serving platforms that handle complex queries with lower latency and maintain answer quality for end users.
Authors
Jinwoo Jeong, Woohyung Choi, Myeongjae Jeon, Jeongseob Ahn
Abstract
Test-time scaling has recently emerged as a powerful approach for improving LLM reasoning by allocating additional computation during inference, substantially enhancing accuracy on challenging tasks such as mathematics and coding. To accelerate the exploration of reasoning paths, recent studies proposed speculative execution. However, we show that supporting speculative execution poses two unique challenges for LLM serving systems: (1) an explosion in the search space of candidate paths and (2) frequent, fine-grained verification tasks for candidates. To address these challenges, this paper proposes SpecScale, a serving system for efficient speculative execution. We introduce three techniques to reconcile the trade-off between latency and computational overhead: (1) early pruning of low-quality candidate paths, (2) deduplicating computation across redundant candidate paths, and (3) deferring fine-grained verification tasks. We evaluate SpecScale on challenging reasoning benchmarks, including MATH and Olympiad. Our results show that SpecScale significantly outperforms both non-speculative and recent speculative approaches, delivering substantial improvements in throughput and latency while preserving answer quality.