The Art of Scaling Test-Time Compute for Large Language Models

Aradhye Agarwal, Ayan Sengupta, Tanmoy Chakraborty

The Art of Scaling Test-Time Compute for Large Language Models: 6 upvotes on Hugging Face Daily Papers, #36 of 47 papers on 2025-12-02. Day-by-day upvote history.

Test-time scaling (TTS) -- the dynamic allocation of compute during inference -- is a promising direction for improving reasoning in large language models (LLMs). However, a systematic comparison of well-known TTS strategies under identical conditions is missing, and the influence of model type and problem difficulty on performance remains unclear. To address these gaps, we conduct the first large-scale study of TTS, spanning over thirty billion tokens generated using eight open-source LLMs (7B to 235B parameters), across four reasoning datasets. We observe three consistent trends: (1) no single TTS strategy universally dominates; (2) reasoning models exhibit distinct trace-quality patterns across problem difficulty and trace length, forming short-horizon and long-horizon categories; and (3) for a given model type, the optimal TTS performance scales monotonically with compute budget. Based on these insights, we provide a practical recipe for selecting the best TTS strategy, considering problem difficulty, model type, and compute budget, providing a practical guide to effective inference-time scaling.

Paper page on Hugging Face · arXiv

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