When Does Reasoning Matter? A Controlled Study of Reasoning's
  Contribution to Model Performance
Authors: name: Nicolas Boizard; name: Hippolyte Gisserot-Boukhlef; name: Kevin El-Haddad; name: Céline Hudelot; name: Pierre Colombo
Published: 2025-09-26 10:53:52

Abstract: Large Language Models (LLMs) with reasoning capabilities have achieved
state-of-the-art performance on a wide range of tasks. Despite its empirical
success, the tasks and model scales at which reasoning becomes effective, as
well as its training and inference costs, remain underexplored. In this work,
we rely on a synthetic data distillation framework to conduct a large-scale
supervised study. We compare Instruction Fine-Tuning (IFT) and reasoning models
of varying sizes, on a wide range of math-centric and general-purpose tasks,
evaluating both multiple-choice and open-ended formats. Our analysis reveals
that reasoning consistently improves model performance, often matching or
surpassing significantly larger IFT systems. Notably, while IFT remains
Pareto-optimal in training and inference costs, reasoning models become
increasingly valuable as model size scales, overcoming IFT performance limits
on reasoning-intensive and open-ended tasks.