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Researchers quantified reasoning redundancy across four frontier LLMs and found that between 61% and 93% of their chain-of-thought steps can be removed while still reaching correct answers, with most models needing only a single critical step for median problems. The redundancy appears to be a structural consequence of how these models are trained with length-agnostic reward systems, not an individual model flaw—suggesting current reasoning approaches inherently over-allocate computation regardless of problem difficulty.
Why it matters: As reasoning models consume massive GPU resources and latency during inference, understanding and potentially eliminating this computational waste could significantly reduce deployment costs and environmental impact while identifying fundamental inefficiencies in how reasoning-capable LLMs are currently trained.
