Multimodal Weekly 82: A Survey on Test-Time Scaling in LLMs
In the 82nd session of Multimodal Weekly, we have an exciting presentation with a survey on test-time scaling in large language models from Fuyuan Lyu and Qiyuan Zhang.
Abstract
As enthusiasm for scaling computation (data and parameters) in the pertaining era gradually diminished, test-time scaling (TTS)—also referred to as “test-time computing”—has emerged as a prominent research focus. Recent studies demonstrate that TTS can further elicit the problem-solving capabilities of large language models (LLMs), enabling significant breakthroughs not only in reasoning-intensive tasks, such as mathematics and coding, but also in general tasks like open-ended Q&A.
However, despite the explosion of recent efforts in this area, there remains an urgent need for a comprehensive survey offering systemic understanding. To fill this gap, we propose a unified, hierarchical framework structured along four orthogonal dimensions of TTS research: what to scale, how to scale, where to scale, and how well to scale.
Building upon this taxonomy, we conduct a holistic review of methods, application scenarios, and assessment aspects, and present an organized decomposition that highlights the unique contributions of individual methods within the broader TTS landscape.
Resources On The Survey
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Multimodal Weekly is organized by Twelve Labs, a startup building multimodal foundation models for video understanding. Learn more about Twelve Labs here: https://twelvelabs.io/