How Much Thinking is Enough? Quantifying and Understanding Redundancy in LLM Reasoning
Reasoning-capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual inspection of their traces reveals extensive reformulation, verification, and circular self-reflection, yet how much of this deliberation is actually necessary has never been measured at scale or explained from first principles. This paper closes both gaps. We formalise reasoning redundancy directly in terms of the reasoning model itself: the redundancy of a correct trace is the largest fraction of its trailing segmented steps that can be truncated while $π$, forced to terminate thinking and emit a final answer, still produces the correct ...
| Authors | Zhiyuan Zhai, Xinkai You, Wenjing Yan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 21 April 2026 |
| Last updated at source | 21 April 2026 |
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