When Does Reasoning Matter? A Controlled Study of Reasoning's Contribution to Model Performance

2025-09-26

AI Summary

This study systematically investigates the effectiveness boundary of reasoning capabilities for enhancing large language model (LLM) performance—specifically, under which task types and model scales reasoning augmentation yields the most significant gains, and how it trades off training and inference costs. Method: We propose a unified evaluation framework based on synthetic data distillation, benchmarking instruction tuning against diverse reasoning paradigms—including chain-of-thought and tree-of-thought—on mathematical reasoning and open-ended generation tasks. Contribution/Results: Reasoning capabilities break through the performance ceiling of instruction-tuned models, especially on reasoning-intensive and open-ended tasks. Compact reasoning-augmented models consistently outperform larger instruction-tuned counterparts, with this advantage amplifying as model scale increases. Crucially, we provide the first quantitative validation of the “reasoning-first” paradigm’s dual superiority in both efficiency and effectiveness.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Evaluating when reasoning improves model performance
• Comparing reasoning models with instruction fine-tuning
• Assessing cost-effectiveness of reasoning versus model scaling

Innovation

• Using synthetic data distillation for controlled study
• Comparing reasoning models with instruction fine-tuning
• Scaling reasoning models to surpass performance limits