Revisiting the Test-Time Scaling of o1-like Models: Do they Truly Possess Test-Time Scaling Capabilities?

TL;DR

This paper investigates whether large language models like o1 truly possess test-time scaling abilities, revealing that longer reasoning chains do not always improve accuracy and proposing a new method to enhance scalability.

Contribution

The study uncovers the relationship between CoT length, self-revision, and accuracy, and introduces Shortest Majority Vote to improve test-time scalability of LLMs.

Findings

Longer CoTs do not consistently improve accuracy.

Self-revision in longer CoTs can degrade performance.

Parallel scaling strategies outperform sequential ones.

Abstract

The advent of test-time scaling in large language models (LLMs), exemplified by OpenAI's o1 series, has advanced reasoning capabilities by scaling computational resource allocation during inference. While successors like QwQ, Deepseek-R1 (R1) and LIMO replicate these advancements, whether these models truly possess test-time scaling capabilities remains underexplored. This study found that longer CoTs of these o1-like models do not consistently enhance accuracy; in fact, correct solutions are often shorter than incorrect ones for the same questions. Further investigation shows this phenomenon is closely related to models' self-revision capabilities - longer CoTs contain more self-revisions, which often lead to performance degradation. We then compare sequential and parallel scaling strategies on QwQ, R1 and LIMO, finding that parallel scaling achieves better coverage and scalability. Based on these insights, we propose Shortest Majority Vote, a method that combines parallel scaling strategies with CoT length characteristics, significantly improving models' test-time scalability compared to conventional majority voting approaches.