Scale or Reason? A Compute-Equivalent Analysis of Reasoning Distillation

The paper investigates whether distilling reasoning traces from large teacher models is worth the extra compute compared to standard instruction fine‑tuning (IFT). By generating paired IFT and reasoning outputs from the same teacher and training student models at five scales, the authors find that, at matched FLOPs, IFT generally lies on or near the Pareto frontier across most configurations. Reasoning traces only reach the frontier on open‑ended tasks for models 7B and larger, and a curriculum mixing 25–50% reasoning data with IFT can capture most of the accuracy benefit at a lower compute cost.

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• llms
• fine-tuning
• efficiency
• benchmarks