LLM Reasoning Redundancy Quantified at Scale

A new study on arXiv quantifies redundancy in reasoning traces of large language models. Researchers formalized redundancy as the fraction of trailing segmented steps that can be truncated while the model still produces the correct answer. Across four frontier reasoning models and two mathematical benchmarks, step-level redundancy ranged from 61% to 93%, with a median of 78%. The work provides the first large-scale measurement and theoretical explanation of reasoning redundancy, highlighting inefficiencies in chain-of-thought processes that incur high latency, GPU time, and energy costs.