How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning
Researchers from arXiv cs.AI introduce a formal definition of reasoning redundancy: the fraction of trailing steps that can be removed while the model still produces the correct answer. Testing four frontier reasoning models on two mathematical benchmarks, they find step-level redundancy ranges from 61% to 93% across eight conditions. In six of eight conditions, the median critical prefix—the minimal number of steps needed—is just a single segmented step. The result holds across different judge families and decreases with problem difficulty. This suggests that current reasoning models expend significant compute on unnecessary deliberation, with implications for latency, GPU cost, and energy consumption. The paper provides the first large-scale quantification and first-principles explanation of this phenomenon.
Developers can reduce inference costs and latency by truncating redundant reasoning steps without sacrificing accuracy.