Are Large Reasoning Models Good Everyday Reasoners? A Comparison with Non-Reasoning Models on Logical Reasoning Tasks

A new arXiv paper (2610.22538v1) finds that large reasoning models (LRMs) using test-time computation often perform worse than non-reasoning models on everyday logical reasoning when that reasoning departs from formal textbook logic. The authors built a benchmark from linguistic and pragmatic reasoning phenomena and compared reasoning and non-reasoning models under controlled prompting; they report that some non-reasoning models with chain-of-thought prompting outperform comparable reasoning models, and that additional prompting instructions provide limited control over reasoning behavior. The authors conclude that current reasoning models do not reliably capture everyday reasoning and point to limitations in post-training, motivating improved training objectives and evaluation across diverse forms of reasoning.

arXiv:2610.22538v1 Announce Type: new Abstract: Large reasoning models LRMs that leverage test-time computation achieve strong performance on mathematical and scientific benchmarks and are increasingly deployed in real-world applications. However, their ability to perform \textit{everyday logical reasoning}, which often departs from textbook formal logic, remains underexplored. In this work, we systematically evaluate how well reasoning models align with everyday reasoning patterns by introducing a benchmark based on linguistic and pragmatic reasoning phenomena. We also compare reasoning models with non-reasoning models under controlled prompting conditions. We find that, in many cases, 1 reasoning models show degraded performance in everyday reasoning when such reasoning deviates from formal logical rules, 2 some non-reasoning models with chain-of-thought prompting outperform comparable reasoning models in everyday reasoning, and 3 prompting with additional instructions provides limited control over reasoning behavior. These findings suggest that current reasoning models do not reliably capture everyday reasoning and point to limitations in post-training, motivating improved training objectives and evaluation across diverse forms of reasoning.